The document proposes applying conditional random fields (CRFs) for legal document summarization. CRFs are used to segment legal documents into seven labeled rhetorical roles. Feature sets are employed to improve CRF performance. A term distribution model and structured domain knowledge are then used to extract key sentences related to the rhetorical categories. The final summaries generated are around 80% accurate compared to expert-generated summaries.
Query Answering Approach Based on Document SummarizationIJMER
The growing of online information obliged the availability of a thorough research in the
domain of automatic text summarization within the Natural Language Processing (NLP)
community.The aim of this paper is to propose a novel approach for a language independent automatic
summarization approach that combines three main approaches. The Rhetorical Structure Theory
(RST), the query processing approach, and the Network Representationapproach (NRA). RST, as a
theory of major aspect for the structure of natural text, is used to extract the semantic relation behind
the text.Query processing approachclassifies the question type and finds the answer in a way that suits
the user’s needs. The NRA is used to create a graph representing the extracted semantic relation. The
output is an answer, which not only responses to the question, but also gives the user an opportunity to
find additional information that is related to the question.We implemented the proposed approach. As a
case study, the implemented approachis applied on Arabic text in the agriculture field. The
implemented approach succeeded in summarizing extension documents according to user's query. The
approach results have been evaluated using Recall, Precision and F-score measures.
This document proposes a method for translating legal sentences that involves two steps: 1) segmenting legal sentences based on their logical structure using conditional random fields, and 2) selecting translation rules for the segmented sentences using maximum entropy and linguistic/contextual features. The method was tested on a corpus of 516 English-Japanese sentence pairs and showed improvements over baseline systems in recognizing logical structures and BLEU scores.
TRANSLATING LEGAL SENTENCE BY SEGMENTATION AND RULE SELECTIONijnlc
This document discusses an approach for translating legal sentences by segmenting them based on their logical structure and then selecting appropriate translation rules. It first uses conditional random fields to recognize the logical structure of legal sentences, which involves a requisite and effectuation. It then segments the sentences based on this structure. Finally, it proposes a maximum entropy model for rule selection in statistical machine translation that incorporates linguistic and contextual information from the segmented sentences. An experiment on English to Japanese legal translation showed improvements over baseline systems.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A COMPARATIVE STUDY OF ROOT-BASED AND STEM-BASED APPROACHES FOR MEASURING THE...acijjournal
Representation of semantic information contained in the words is needed for any Arabic Text Mining
applications. More precisely, the purpose is to better take into account the semantic dependencies
between words expressed by the co-occurrence frequencies of these words. There have been many
proposals to compute similarities between words based on their distributions in contexts. In this paper,
we compare and contrast the effect of two preprocessing techniques applied to Arabic corpus: Rootbased (Stemming), and Stem-based (Light Stemming) approaches for measuring the similarity between
Arabic words with the well known abstractive model -Latent Semantic Analysis (LSA)- with a wide
variety of distance functions and similarity measures, such as the Euclidean Distance, Cosine Similarity,
Jaccard Coefficient, and the Pearson Correlation Coefficient. The obtained results show that, on the one
hand, the variety of the corpus produces more accurate results; on the other hand, the Stem-based
approach outperformed the Root-based one because this latter affects the words meanings.
ONTOLOGICAL TREE GENERATION FOR ENHANCED INFORMATION RETRIEVALijaia
This document proposes a methodology to extract information from big data sources like course handouts and directories and represent it in a graphical, ontological tree format. Keywords are extracted from documents using natural language processing techniques and used to generate a hierarchical tree based on the DMOZ open directory project. The trees provide a comprehensive overview of document content and structure. The method is implemented using Python for natural language processing and Java for visualization. Evaluation on computer science course handouts shows the trees accurately represent topic coverage and depth. Future work aims to increase the number of keywords extracted.
A template based algorithm for automatic summarization and dialogue managemen...eSAT Journals
Abstract This paper describes an automated approach for extracting significant and useful events from unstructured text. The goal of research is to come out with a methodology which helps in extracting important events such as dates, places, and subjects of interest. It would be also convenient if the methodology helps in presenting the users with a shorter version of the text which contain all non-trivial information. We also discuss implementation of algorithms which exactly does this task, developed by us. Key Words: Cosine Similarity, Information, Natural Language, Summarization, Text Mining
This document summarizes an article that proposes an automatic text summarization technique using feature terms to calculate sentence relevance. The technique uses both statistical and linguistic methods to identify semantically important sentences for creating a generic summary. It determines the relevance of sentences based on feature term ranks and performs semantic analysis of sentences with the highest ranks to select those most important for the summary. The performance is evaluated by comparing summaries to those created by human evaluators.
Query Answering Approach Based on Document SummarizationIJMER
The growing of online information obliged the availability of a thorough research in the
domain of automatic text summarization within the Natural Language Processing (NLP)
community.The aim of this paper is to propose a novel approach for a language independent automatic
summarization approach that combines three main approaches. The Rhetorical Structure Theory
(RST), the query processing approach, and the Network Representationapproach (NRA). RST, as a
theory of major aspect for the structure of natural text, is used to extract the semantic relation behind
the text.Query processing approachclassifies the question type and finds the answer in a way that suits
the user’s needs. The NRA is used to create a graph representing the extracted semantic relation. The
output is an answer, which not only responses to the question, but also gives the user an opportunity to
find additional information that is related to the question.We implemented the proposed approach. As a
case study, the implemented approachis applied on Arabic text in the agriculture field. The
implemented approach succeeded in summarizing extension documents according to user's query. The
approach results have been evaluated using Recall, Precision and F-score measures.
This document proposes a method for translating legal sentences that involves two steps: 1) segmenting legal sentences based on their logical structure using conditional random fields, and 2) selecting translation rules for the segmented sentences using maximum entropy and linguistic/contextual features. The method was tested on a corpus of 516 English-Japanese sentence pairs and showed improvements over baseline systems in recognizing logical structures and BLEU scores.
TRANSLATING LEGAL SENTENCE BY SEGMENTATION AND RULE SELECTIONijnlc
This document discusses an approach for translating legal sentences by segmenting them based on their logical structure and then selecting appropriate translation rules. It first uses conditional random fields to recognize the logical structure of legal sentences, which involves a requisite and effectuation. It then segments the sentences based on this structure. Finally, it proposes a maximum entropy model for rule selection in statistical machine translation that incorporates linguistic and contextual information from the segmented sentences. An experiment on English to Japanese legal translation showed improvements over baseline systems.
International Journal of Computational Engineering Research(IJCER) ijceronline
International Journal of Computational Engineering Research(IJCER) is an intentional online Journal in English monthly publishing journal. This Journal publish original research work that contributes significantly to further the scientific knowledge in engineering and Technology.
A COMPARATIVE STUDY OF ROOT-BASED AND STEM-BASED APPROACHES FOR MEASURING THE...acijjournal
Representation of semantic information contained in the words is needed for any Arabic Text Mining
applications. More precisely, the purpose is to better take into account the semantic dependencies
between words expressed by the co-occurrence frequencies of these words. There have been many
proposals to compute similarities between words based on their distributions in contexts. In this paper,
we compare and contrast the effect of two preprocessing techniques applied to Arabic corpus: Rootbased (Stemming), and Stem-based (Light Stemming) approaches for measuring the similarity between
Arabic words with the well known abstractive model -Latent Semantic Analysis (LSA)- with a wide
variety of distance functions and similarity measures, such as the Euclidean Distance, Cosine Similarity,
Jaccard Coefficient, and the Pearson Correlation Coefficient. The obtained results show that, on the one
hand, the variety of the corpus produces more accurate results; on the other hand, the Stem-based
approach outperformed the Root-based one because this latter affects the words meanings.
ONTOLOGICAL TREE GENERATION FOR ENHANCED INFORMATION RETRIEVALijaia
This document proposes a methodology to extract information from big data sources like course handouts and directories and represent it in a graphical, ontological tree format. Keywords are extracted from documents using natural language processing techniques and used to generate a hierarchical tree based on the DMOZ open directory project. The trees provide a comprehensive overview of document content and structure. The method is implemented using Python for natural language processing and Java for visualization. Evaluation on computer science course handouts shows the trees accurately represent topic coverage and depth. Future work aims to increase the number of keywords extracted.
A template based algorithm for automatic summarization and dialogue managemen...eSAT Journals
Abstract This paper describes an automated approach for extracting significant and useful events from unstructured text. The goal of research is to come out with a methodology which helps in extracting important events such as dates, places, and subjects of interest. It would be also convenient if the methodology helps in presenting the users with a shorter version of the text which contain all non-trivial information. We also discuss implementation of algorithms which exactly does this task, developed by us. Key Words: Cosine Similarity, Information, Natural Language, Summarization, Text Mining
This document summarizes an article that proposes an automatic text summarization technique using feature terms to calculate sentence relevance. The technique uses both statistical and linguistic methods to identify semantically important sentences for creating a generic summary. It determines the relevance of sentences based on feature term ranks and performs semantic analysis of sentences with the highest ranks to select those most important for the summary. The performance is evaluated by comparing summaries to those created by human evaluators.
This document describes a method for sentence similarity based text summarization using clusters. It involves preprocessing text, extracting primitives from sentences, linking primitives, computing sentence similarity, merging similarity values, clustering similar sentences, and extracting a representative sentence from each cluster to generate a summary. Key steps include identifying common elements (primitives) between sentences, representing sentences as vectors of primitives, computing similarity based on shared primitives, clustering similar sentences, pruning clusters to remove dissimilar sentences, ranking clusters by importance, and selecting a representative sentence from each cluster for the summary. The goal is to automatically generate a short summary that captures the essential information from a collection of documents or text on the same topic.
Text Segmentation for Online Subjective Examination using Machine LearningIRJET Journal
This document discusses using k-Nearest Neighbor (K-NN) machine learning for text segmentation of online exams. K-NN is an instance-based learning method that computes similarity between feature vectors to determine similarity between texts. The goal is to implement natural language processing using text segmentation, which provides benefits. It reviews related work applying various machine learning methods like K-NN, support vector machines, decision trees to tasks like text categorization and clustering.
Rhetorical Sentence Classification for Automatic Title Generation in Scientif...TELKOMNIKA JOURNAL
In this paper, we proposed a work on rhetorical corpus construction and sentence classification
model experiment that specifically could be incorporated in automatic paper title generation task for
scientific article. Rhetorical classification is treated as sequence labeling. Rhetorical sentence classification
model is useful in task which considers document’s discourse structure. We performed experiments using
two domains of datasets: computer science (CS dataset), and chemistry (GaN dataset). We evaluated the
models using 10-fold-cross validation (0.70-0.79 weighted average F-measure) as well as on-the-run
(0.30-0.36 error rate at best). We argued that our models performed best when handled using SMOTE
filter for imbalanced data.
Taxonomy extraction from automotive natural language requirements using unsup...ijnlc
In this paper we present a novel approach to semi-automatically learn concept hierarchies from natural
language requirements of the automotive industry. The approach is based on the distributional hypothesis
and the special characteristics of domain-specific German compounds. We extract taxonomies by using
clustering techniques in combination with general thesauri. Such a taxonomy can be used to support
requirements engineering in early stages by providing a common system understanding and an agreedupon
terminology. This work is part of an ontology-driven requirements engineering process, which builds
on top of the taxonomy. Evaluation shows that this taxonomy extraction approach outperforms common
hierarchical clustering techniques.
TEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHSijcsit
The paper proposes a method to check whether two documents are having textual plagiarism or not. This
technique is based on Extrinsic Plagiarism Detection. The technique that is applied here is quite similar to
the one that is used to grade the short answers. The triplets and associated information are extracted from
both texts and are stored in friendship matrices. Then these two friendship matrices are compared and a
similarity percentage is calculated. This similarity percentage is used to take decisions
AN EFFICIENT APPROACH TO IMPROVE ARABIC DOCUMENTS CLUSTERING BASED ON A NEW K...csandit
Document Clustering algorithms goal is to create clusters that are coherent internally, but
clearly different from each other. The useful expressions in the documents is often accompanied
by a large amount of noise that is caused by the use of unnecessary words, so it is indispensable
to eliminate it and keeping just the useful information.
Keyphrases extraction systems in Arabic are new phenomena. A number of Text Mining
applications can use it to improve her results. The Keyphrases are defined as phrases that
capture the main topics discussed in document; they offer a brief and precise summary of
document content. Therefore, it can be a good solution to get rid of the existent noise from
documents.
In this paper, we propose a new method to solve the problem cited above especially for Arabic
language documents, which is one of the most complex languages, by using a new Keyphrases
extraction algorithm based on the Suffix Tree data structure (KpST). To evaluate our approach,
we conduct an experimental study on Arabic Documents Clustering using the most popular
approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with seven linkage
techniques and a variety of distance functions and similarity measures to perform Arabic
Document Clustering task. The obtained results show that our approach for extracting
Keyphrases improves the clustering results.
IRJET- A Survey Paper on Text Summarization MethodsIRJET Journal
This document summarizes various approaches for automatic text summarization, including extractive and abstractive methods. It discusses early surface-level approaches from the 1950s that identified important sentences based on word frequency. It also reviews corpus-based, cohesion-based, rhetoric-based, and graph-based approaches. The document then examines single document summarization techniques like naive Bayes methods, log-linear models, and deep natural language analysis. It concludes with a discussion of multi-document summarization, including abstraction and information fusion as well as graph spreading activation approaches. The goal of the survey is to provide an overview of the major existing methods that have been used for automatic text summarization.
NAMED ENTITY RECOGNITION IN TURKISH USING ASSOCIATION MEASURESacijjournal
Named Entity Recognition which is an important subject of Natural Language Processing is a key technology of information extraction, information retrieval, question answering and other text processing applications. In this study, we evaluate previously well-established association measures as an initial
attempt to extract two-worded named entities in a Turkish corpus. Furthermore we propose a new association measure, and compare it with the other methods. The evaluation of these methods is performed by precision and recall measures.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
Improvement of Text Summarization using Fuzzy Logic Based MethodIOSR Journals
The document describes a method for improving text summarization using fuzzy logic. It proposes using fuzzy logic to determine the importance of sentences based on calculated feature scores. Eight features are used to score sentences, including title words, length, term frequency, position, and similarity. Sentences are then ranked based on their fuzzy logic-determined scores. The highest scoring sentences are extracted to create a summary. An evaluation of summaries generated using this fuzzy logic method found it performed better than other summarizers in accurately reflecting the content and order of human-generated reference summaries. The method could be expanded to multi-document summarization and automatic selection of fuzzy rules based on input type.
This document summarizes a research paper that proposes and compares fuzzy and Naive Bayes models for detecting obfuscated plagiarism in Marathi language texts. It first provides background on plagiarism detection and describes different types of plagiarism, including obfuscated plagiarism. It then presents the fuzzy semantic similarity model, which uses fuzzy logic rules and semantic relatedness between words to calculate similarity scores between texts. Next, it describes the Naive Bayes model for plagiarism detection using Bayes' theorem. The paper compares the performance of the fuzzy and Naive Bayes models on precision, recall, F-measure and granularity. It finds that the Naive Bayes model provides more accurate detection of obfuscated plagiar
1) The document discusses different clustering algorithms for text summarization including hierarchical clustering, query-based summarization, graph theoretic clustering, fuzzy c-means clustering, and DBSCAN clustering.
2) These algorithms are evaluated based on performance parameters like precision, recall, time complexity, space complexity, and summary quality.
3) The algorithm found to perform best based on these evaluations will be suggested as the better algorithm for query-dependent text document summarization.
A NOVEL APPROACH FOR WORD RETRIEVAL FROM DEVANAGARI DOCUMENT IMAGESijnlc
Large amount of information is lying dormant in historical documents and manuscripts. This information would go futile if not stored in digital form. Searching some relevant information from these scanned images would ideally require converting these document images to text form by doing optical character
recognition (OCR). For indigenous scripts of India, there are very few OCRs that can successfully recognize printed text images of varying quality, size, style and font. An alternate approach using word spotting can be effective to access large collections of document images. We propose a word spotting
technique based on codes for matching the word images of Devanagari script. The shape information is utilised for generating integer codes for words in the document image and these codes are matched for final retrieval of relevant documents. The technique is illustrated using Marathi document images.
A Newly Proposed Technique for Summarizing the Abstractive Newspapers’ Articl...mlaij
In this new era, where tremendous information is available on the internet, it is of most important to
provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult
for human beings to manually extract the summary of large documents of text. Therefore, there is a
problem of searching for relevant documents from the number of documents available, and absorbing
relevant information from it. In order to solve the above two problems, the automatic text summarization is
very much necessary. Text summarization is the process of identifying the most important meaningful
information in a document or set of related documents and compressing them into a shorter version
preserving its overall meanings. More specific, Abstractive Text Summarization (ATS), is the task of
constructing summary sentences by merging facts from different source sentences and condensing them
into a shorter representation while preserving information content and overall meaning. This Paper
introduces a newly proposed technique for Summarizing the abstractive newspapers’ articles based on
deep learning.
This summarizes an academic paper that proposes an automatic ontology creation method for classifying research papers. It uses text mining techniques like classification and clustering algorithms. It first builds a research ontology by extracting keywords and patterns from previous papers. It then uses a decision tree algorithm to classify new papers into disciplines defined in the ontology. The classified papers are then clustered based on similarities to group them. The method was tested on a dataset of 100 papers and achieved average precision of 85.7% for term-based and 89.3% for pattern-based keyword extraction.
8 efficient multi-document summary generation using neural networkINFOGAIN PUBLICATION
This paper proposes a multi-document summarization system that uses bisect k-means clustering, an optimal merge function, and a neural network. The system first preprocesses input documents through stemming and removing stop words. It then applies bisect k-means clustering to group similar sentences. The clusters are merged using an optimal merge function to find important keywords. The NEWSUM algorithm is used to generate a primary summary for each keyword. A neural network trained on sentence classifications is then used to classify sentences in the primary summary as positive or negative. Only positively classified sentences are included in the final summary to improve accuracy. The system aims to generate a concise and accurate summary in a short period of time from multiple documents on a given topic.
Mining Users Rare Sequential Topic Patterns from Tweets based on Topic Extrac...IRJET Journal
This paper proposes a method to mine rare sequential topic patterns (URSTPs) from tweet data. It involves preprocessing tweets to extract topics, identifying user sessions, generating sequential topic pattern (STP) candidates, and selecting URSTPs based on rarity analysis. Experiments show the approach can identify special users and interpretable URSTPs, indicating users' characteristics. The paper aims to capture personalized and abnormal user behaviors through sequential relationships between extracted topics from successive tweets.
Library spaces must balance the needs of different user groups and activities. They should provide areas for both quiet study and group collaboration. Furniture and signage should be arranged logically to guide traffic flow. Displays and natural lighting can make the space more inviting. As technologies change, libraries must design flexible spaces that can accommodate unknown future needs. Cafes, galleries and other amenities can attract more users and legitimize the library's role in the digital age.
La organización Mutepaz ha desarrollado varias propuestas para construir la paz entre el pueblo Wayúu, incluyendo la creación de aulas escolares gratuitas y un centro de pensamiento para enseñar la lengua materna de los Wayúu. Mutepaz también ha organizado protestas pacíficas para defender los derechos de la mujer y brinda apoyo a víctimas de violencia.
Cut Off Vampire Appliances' Phantom Loads with Tripp LiteTripp Lite
Phantom power is costing you up to $200 per year! Cut power to electronics that are sucking you dry. Items like computers, media players, cell phone chargers and lamps may be in the off position, but they're still using electricity while plugged in. With Tripp Lite's ECO-Surge Protectors and ECO-UPS Systems, you can banish phantom loads with power save outlets.
What other pieces in your house are vampires?
This document describes a method for sentence similarity based text summarization using clusters. It involves preprocessing text, extracting primitives from sentences, linking primitives, computing sentence similarity, merging similarity values, clustering similar sentences, and extracting a representative sentence from each cluster to generate a summary. Key steps include identifying common elements (primitives) between sentences, representing sentences as vectors of primitives, computing similarity based on shared primitives, clustering similar sentences, pruning clusters to remove dissimilar sentences, ranking clusters by importance, and selecting a representative sentence from each cluster for the summary. The goal is to automatically generate a short summary that captures the essential information from a collection of documents or text on the same topic.
Text Segmentation for Online Subjective Examination using Machine LearningIRJET Journal
This document discusses using k-Nearest Neighbor (K-NN) machine learning for text segmentation of online exams. K-NN is an instance-based learning method that computes similarity between feature vectors to determine similarity between texts. The goal is to implement natural language processing using text segmentation, which provides benefits. It reviews related work applying various machine learning methods like K-NN, support vector machines, decision trees to tasks like text categorization and clustering.
Rhetorical Sentence Classification for Automatic Title Generation in Scientif...TELKOMNIKA JOURNAL
In this paper, we proposed a work on rhetorical corpus construction and sentence classification
model experiment that specifically could be incorporated in automatic paper title generation task for
scientific article. Rhetorical classification is treated as sequence labeling. Rhetorical sentence classification
model is useful in task which considers document’s discourse structure. We performed experiments using
two domains of datasets: computer science (CS dataset), and chemistry (GaN dataset). We evaluated the
models using 10-fold-cross validation (0.70-0.79 weighted average F-measure) as well as on-the-run
(0.30-0.36 error rate at best). We argued that our models performed best when handled using SMOTE
filter for imbalanced data.
Taxonomy extraction from automotive natural language requirements using unsup...ijnlc
In this paper we present a novel approach to semi-automatically learn concept hierarchies from natural
language requirements of the automotive industry. The approach is based on the distributional hypothesis
and the special characteristics of domain-specific German compounds. We extract taxonomies by using
clustering techniques in combination with general thesauri. Such a taxonomy can be used to support
requirements engineering in early stages by providing a common system understanding and an agreedupon
terminology. This work is part of an ontology-driven requirements engineering process, which builds
on top of the taxonomy. Evaluation shows that this taxonomy extraction approach outperforms common
hierarchical clustering techniques.
TEXT PLAGIARISM CHECKER USING FRIENDSHIP GRAPHSijcsit
The paper proposes a method to check whether two documents are having textual plagiarism or not. This
technique is based on Extrinsic Plagiarism Detection. The technique that is applied here is quite similar to
the one that is used to grade the short answers. The triplets and associated information are extracted from
both texts and are stored in friendship matrices. Then these two friendship matrices are compared and a
similarity percentage is calculated. This similarity percentage is used to take decisions
AN EFFICIENT APPROACH TO IMPROVE ARABIC DOCUMENTS CLUSTERING BASED ON A NEW K...csandit
Document Clustering algorithms goal is to create clusters that are coherent internally, but
clearly different from each other. The useful expressions in the documents is often accompanied
by a large amount of noise that is caused by the use of unnecessary words, so it is indispensable
to eliminate it and keeping just the useful information.
Keyphrases extraction systems in Arabic are new phenomena. A number of Text Mining
applications can use it to improve her results. The Keyphrases are defined as phrases that
capture the main topics discussed in document; they offer a brief and precise summary of
document content. Therefore, it can be a good solution to get rid of the existent noise from
documents.
In this paper, we propose a new method to solve the problem cited above especially for Arabic
language documents, which is one of the most complex languages, by using a new Keyphrases
extraction algorithm based on the Suffix Tree data structure (KpST). To evaluate our approach,
we conduct an experimental study on Arabic Documents Clustering using the most popular
approach of Hierarchical algorithms: Agglomerative Hierarchical algorithm with seven linkage
techniques and a variety of distance functions and similarity measures to perform Arabic
Document Clustering task. The obtained results show that our approach for extracting
Keyphrases improves the clustering results.
IRJET- A Survey Paper on Text Summarization MethodsIRJET Journal
This document summarizes various approaches for automatic text summarization, including extractive and abstractive methods. It discusses early surface-level approaches from the 1950s that identified important sentences based on word frequency. It also reviews corpus-based, cohesion-based, rhetoric-based, and graph-based approaches. The document then examines single document summarization techniques like naive Bayes methods, log-linear models, and deep natural language analysis. It concludes with a discussion of multi-document summarization, including abstraction and information fusion as well as graph spreading activation approaches. The goal of the survey is to provide an overview of the major existing methods that have been used for automatic text summarization.
NAMED ENTITY RECOGNITION IN TURKISH USING ASSOCIATION MEASURESacijjournal
Named Entity Recognition which is an important subject of Natural Language Processing is a key technology of information extraction, information retrieval, question answering and other text processing applications. In this study, we evaluate previously well-established association measures as an initial
attempt to extract two-worded named entities in a Turkish corpus. Furthermore we propose a new association measure, and compare it with the other methods. The evaluation of these methods is performed by precision and recall measures.
This paper proposes Natural language based Discourse Analysis method used for extracting
information from the news article of different domain. The Discourse analysis used the Rhetorical Structure
theory which is used to find coherent group of text which are most prominent for extracting information
from text. RST theory used the Nucleus- Satellite concept for finding most prominent text from the text
document. After Discourse analysis the text analysis has been done for extracting domain related object
and relates this object. For extracting the information knowledge based system has been used which
consist of domain dictionary .The domain dictionary has a bag of words for domain. The system is
evaluated according gold-of-art analysis and human decision for extracted information.
Improvement of Text Summarization using Fuzzy Logic Based MethodIOSR Journals
The document describes a method for improving text summarization using fuzzy logic. It proposes using fuzzy logic to determine the importance of sentences based on calculated feature scores. Eight features are used to score sentences, including title words, length, term frequency, position, and similarity. Sentences are then ranked based on their fuzzy logic-determined scores. The highest scoring sentences are extracted to create a summary. An evaluation of summaries generated using this fuzzy logic method found it performed better than other summarizers in accurately reflecting the content and order of human-generated reference summaries. The method could be expanded to multi-document summarization and automatic selection of fuzzy rules based on input type.
This document summarizes a research paper that proposes and compares fuzzy and Naive Bayes models for detecting obfuscated plagiarism in Marathi language texts. It first provides background on plagiarism detection and describes different types of plagiarism, including obfuscated plagiarism. It then presents the fuzzy semantic similarity model, which uses fuzzy logic rules and semantic relatedness between words to calculate similarity scores between texts. Next, it describes the Naive Bayes model for plagiarism detection using Bayes' theorem. The paper compares the performance of the fuzzy and Naive Bayes models on precision, recall, F-measure and granularity. It finds that the Naive Bayes model provides more accurate detection of obfuscated plagiar
1) The document discusses different clustering algorithms for text summarization including hierarchical clustering, query-based summarization, graph theoretic clustering, fuzzy c-means clustering, and DBSCAN clustering.
2) These algorithms are evaluated based on performance parameters like precision, recall, time complexity, space complexity, and summary quality.
3) The algorithm found to perform best based on these evaluations will be suggested as the better algorithm for query-dependent text document summarization.
A NOVEL APPROACH FOR WORD RETRIEVAL FROM DEVANAGARI DOCUMENT IMAGESijnlc
Large amount of information is lying dormant in historical documents and manuscripts. This information would go futile if not stored in digital form. Searching some relevant information from these scanned images would ideally require converting these document images to text form by doing optical character
recognition (OCR). For indigenous scripts of India, there are very few OCRs that can successfully recognize printed text images of varying quality, size, style and font. An alternate approach using word spotting can be effective to access large collections of document images. We propose a word spotting
technique based on codes for matching the word images of Devanagari script. The shape information is utilised for generating integer codes for words in the document image and these codes are matched for final retrieval of relevant documents. The technique is illustrated using Marathi document images.
A Newly Proposed Technique for Summarizing the Abstractive Newspapers’ Articl...mlaij
In this new era, where tremendous information is available on the internet, it is of most important to
provide the improved mechanism to extract the information quickly and most efficiently. It is very difficult
for human beings to manually extract the summary of large documents of text. Therefore, there is a
problem of searching for relevant documents from the number of documents available, and absorbing
relevant information from it. In order to solve the above two problems, the automatic text summarization is
very much necessary. Text summarization is the process of identifying the most important meaningful
information in a document or set of related documents and compressing them into a shorter version
preserving its overall meanings. More specific, Abstractive Text Summarization (ATS), is the task of
constructing summary sentences by merging facts from different source sentences and condensing them
into a shorter representation while preserving information content and overall meaning. This Paper
introduces a newly proposed technique for Summarizing the abstractive newspapers’ articles based on
deep learning.
This summarizes an academic paper that proposes an automatic ontology creation method for classifying research papers. It uses text mining techniques like classification and clustering algorithms. It first builds a research ontology by extracting keywords and patterns from previous papers. It then uses a decision tree algorithm to classify new papers into disciplines defined in the ontology. The classified papers are then clustered based on similarities to group them. The method was tested on a dataset of 100 papers and achieved average precision of 85.7% for term-based and 89.3% for pattern-based keyword extraction.
8 efficient multi-document summary generation using neural networkINFOGAIN PUBLICATION
This paper proposes a multi-document summarization system that uses bisect k-means clustering, an optimal merge function, and a neural network. The system first preprocesses input documents through stemming and removing stop words. It then applies bisect k-means clustering to group similar sentences. The clusters are merged using an optimal merge function to find important keywords. The NEWSUM algorithm is used to generate a primary summary for each keyword. A neural network trained on sentence classifications is then used to classify sentences in the primary summary as positive or negative. Only positively classified sentences are included in the final summary to improve accuracy. The system aims to generate a concise and accurate summary in a short period of time from multiple documents on a given topic.
Mining Users Rare Sequential Topic Patterns from Tweets based on Topic Extrac...IRJET Journal
This paper proposes a method to mine rare sequential topic patterns (URSTPs) from tweet data. It involves preprocessing tweets to extract topics, identifying user sessions, generating sequential topic pattern (STP) candidates, and selecting URSTPs based on rarity analysis. Experiments show the approach can identify special users and interpretable URSTPs, indicating users' characteristics. The paper aims to capture personalized and abnormal user behaviors through sequential relationships between extracted topics from successive tweets.
Library spaces must balance the needs of different user groups and activities. They should provide areas for both quiet study and group collaboration. Furniture and signage should be arranged logically to guide traffic flow. Displays and natural lighting can make the space more inviting. As technologies change, libraries must design flexible spaces that can accommodate unknown future needs. Cafes, galleries and other amenities can attract more users and legitimize the library's role in the digital age.
La organización Mutepaz ha desarrollado varias propuestas para construir la paz entre el pueblo Wayúu, incluyendo la creación de aulas escolares gratuitas y un centro de pensamiento para enseñar la lengua materna de los Wayúu. Mutepaz también ha organizado protestas pacíficas para defender los derechos de la mujer y brinda apoyo a víctimas de violencia.
Cut Off Vampire Appliances' Phantom Loads with Tripp LiteTripp Lite
Phantom power is costing you up to $200 per year! Cut power to electronics that are sucking you dry. Items like computers, media players, cell phone chargers and lamps may be in the off position, but they're still using electricity while plugged in. With Tripp Lite's ECO-Surge Protectors and ECO-UPS Systems, you can banish phantom loads with power save outlets.
What other pieces in your house are vampires?
Cooperative relationships between school libraries and public libraries have the potential to enrich school students' learning and advance lifelong learning in the wider community. Research findings.
Gravitation and Orbit describes the basics of gravitation and orbits. Any two bodies with mass attract each other with a gravitational force proportional to their masses and inversely proportional to the distance between them. Orbits occur as objects fall towards a central body but have enough tangential velocity to miss it and continue falling indefinitely. The speed of satellites in orbit can be calculated based on their distance and the gravitational force exerted by the central body. Gravity also causes tides on Earth and gravitational fields can be approximated as uniform close to the surface, becoming stronger closer to the source mass. Prolonged time in microgravity conditions like space can lead to severe bone and muscle loss in humans.
Our understanding of atomic structure has changed significantly over time. Originally, Dalton proposed that atoms were indivisible spheres (1808). Later, experiments revealed atoms have internal structure including a small, dense nucleus and subatomic particles like electrons, protons, and neutrons. The modern atomic model depicts atoms as a small, positively charged nucleus surrounded by negatively charged electrons.
Digital Initiatives at the State Library of NC (NCLA Conference 2009)guest591492
The State Library of North Carolina is working on several digital projects to increase transparency and access to government information. These include digitizing historical state publications, census records, and web archives. They are also developing recommendations for preserving "born-digital" state government records and collaborating with agencies to add to their digital collections. Future plans include expanding social media harvesting and educating agencies on digital preservation.
I cet feng an clean truck program in china 2011 baltimoreCALSTART
Presentation by iCET's Feng An on China's Clean Truck Program.
The US-China Clean Truck Forum (China Clean Truck) aims to facilitate advanced truck technology cooperation and exports by providing market and policy information, targeted assistance, and partnership development. CALSTART – together with partners GTSAC (Green Truck Strategic Alliance of China) and iCET (Innovation Center for Energy and Transportation) – created the cooperative program to meet these goals, in a program partnership with the US Department of Commerce and its International Trade Administration. Among other things, China Clean Truck will include a China Clean Truck Markets and Business Guide for US companies as well as face-to-face symposiums in China with leading companies and agencies.
I will discuss the formation and subsequent growth of IRDiRC into an organization with nearly 40 public and private funder members who have collectively pledged over 1 billion euros for rare disease research. I will also present the goals of IRDiRC, the plan that has been developed to achieve them, and the progress that has been made thus far. Finally, I will explore how additional organizations can take part in this international collaborative effort
Digital Marketing Case Discussion - AlpenlibbeDigital Vidya
The document outlines a digital marketing campaign for Alpenliebe confectionery to increase awareness, registrations for auditions, sampling, and engage a younger target group. The plan involves generating awareness through Facebook, online gaming sites, YouTube and a microsite with rich content. It aims to drive walk-ins, PR buzz, and content for on-air promos and social media marketing by engaging users through mobile apps and creating a community.
La rúbrica evalúa un ensayo de la unidad 1 de Biología y Morfología Vegetal en 5 categorías: 1) Datos generales, 2) Redacción, 3) Vocabulario, 4) Intención y 5) Nivel de desarrollo del tema. Describe los niveles de logro de excelente a mínimo aprobatorio e indica los criterios de evaluación para cada categoría.
Anaphora Resolution in Business Process Requirement Engineering IJECEIAES
Anaphora resolution (AR) is one of the most important tasks in natural language processing which focuses on the problem of resolving what a pronoun, or a noun phrase refers to. Moreover, AR plays an essential role when dealing with business process textual description, either when trying to discover the process model from the text, or when validating an existing model. It helps these systems in discovering the core components in any process model (actors and objects).In this paper, we propose a domain specific AR system. The approach starts by automatically generating the concept map of the text, then the system uses this map to resolve references using the syntactic and semantic relations in the concept map. The approach outperforms the state-of-the art performance in the domain of business process texts with more than 73% accuracy. In addition, this approach could be easily adopted to resolve references in other domains.
Data mining is the knowledge discovery in databases and the gaol is to extract patterns and knowledge from
large amounts of data. The important term in data mining is text mining. Text mining extracts the quality
information highly from text. Statistical pattern learning is used to high quality information. High –quality in
text mining defines the combinations of relevance, novelty and interestingness. Tasks in text mining are text
categorization, text clustering, entity extraction and sentiment analysis. Applications of natural language
processing and analytical methods are highly preferred to turn
The document summarizes text mining techniques in data mining. It discusses common text mining tasks like text categorization, clustering, and entity extraction. It also reviews several text mining algorithms and techniques, including information extraction, clustering, classification, and information visualization. Several literature papers applying these techniques to domains like movie reviews, research proposals, and e-commerce are also summarized. The document concludes that text mining can extract useful patterns from unstructured text through techniques like clustering, classification, and information extraction.
This document presents a framework for automatically generating entity-relationship (ER) diagrams from natural language text input. It involves five main modules: 1) text preprocessing and summary generation, 2) translating the summary to a Semantic Business Vocabulary and Rules (SBVR) format, 3) part-of-speech tagging, 4) extracting ER diagram requirements by identifying entities, relationships, and attributes, and 5) generating an XMI file that can be imported into a UML modeling tool to visualize the generated ER diagram. Keywords are extracted from the input text using term frequency, and sentences are scored and selected for the summary based on important keywords and nouns. The framework aims to reduce the complexity of manually creating ER diagrams by
Query expansion using novel use case scenario relationship for finding featur...IJECEIAES
Feature location is a technique for determining source code that implements specific features in software. It developed to help minimize effort on program comprehension. The main challenge of feature location research is how to bridge the gap between abstract keywords in use cases and detail in source code. The use case scenarios are software requirements artifacts that state the input, logic, rules, actor, and output of a function in the software. The sentence on use case scenario is sometimes described another sentence in other use case scenario. This study contributes to creating expansion queries in feature locations by finding the relationship between use case scenarios. The relationships include inner association, outer association and intratoken association. The research employs latent Dirichlet allocation (LDA) to create model topics on source code. Query expansion using inner, outer and intratoken was tested for finding feature locations on a Java-based open-source project. The best precision rate was 50%. The best recall was 100%, which was found in several use case scenarios implemented in a few files. The best average precision rate was 16.7%, which was found in inner association experiments. The best average recall rate was 68.3%, which was found in all compound association experiments.
A hybrid composite features based sentence level sentiment analyzerIAESIJAI
Current lexica and machine learning based sentiment analysis approaches
still suffer from a two-fold limitation. First, manual lexicon construction and
machine training is time consuming and error-prone. Second, the
prediction’s accuracy entails sentences and their corresponding training text
should fall under the same domain. In this article, we experimentally
evaluate four sentiment classifiers, namely support vector machines (SVMs),
Naive Bayes (NB), logistic regression (LR) and random forest (RF). We
quantify the quality of each of these models using three real-world datasets
that comprise 50,000 movie reviews, 10,662 sentences, and 300 generic
movie reviews. Specifically, we study the impact of a variety of natural
language processing (NLP) pipelines on the quality of the predicted
sentiment orientations. Additionally, we measure the impact of incorporating
lexical semantic knowledge captured by WordNet on expanding original
words in sentences. Findings demonstrate that the utilizing different NLP
pipelines and semantic relationships impacts the quality of the sentiment
analyzers. In particular, results indicate that coupling lemmatization and
knowledge-based n-gram features proved to produce higher accuracy results.
With this coupling, the accuracy of the SVM classifier has improved to
90.43%, while it was 86.83%, 90.11%, 86.20%, respectively using the three
other classifiers.
A Novel Method for An Intelligent Based Voice Meeting System Using Machine Le...IRJET Journal
This document proposes a method for an intelligent voice meeting system using machine learning. It involves speech recognition to convert speech to text, followed by text summarization. The proposed method achieved high accuracy, precision, and F1 scores in experiments. It provides an effective way to transcribe and summarize meetings by utilizing speech recognition and text summarization technologies. The system architecture involves speech to text conversion, text summarization, storage in a database, and display of summarized text to users. The experimental results demonstrate the system's ability to accurately transcribe and summarize spoken content.
Knowledge Graph and Similarity Based Retrieval Method for Query Answering SystemIRJET Journal
This document proposes a knowledge graph and question answering system to extract and analyze information from large volumes of unstructured data like annual reports. It discusses using natural language processing techniques like named entity recognition with spaCy and dependency parsing to extract entity-relation pairs from text and construct a knowledge graph. For question answering, it analyzes user queries with similar NLP approaches and then matches query triplets to the knowledge graph to retrieve answers, combining information retrieval and trained classifiers. The proposed system aims to provide faster understanding and analysis of complex, unstructured data for professionals.
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSERkevig
We aim to model an adaptive log file parser. As the content of log files often evolves over time, we
established a dynamic statistical model which learns and adapts processing and parsing rules. First, we
limit the amount of unstructured text by clustering based on semantics of log file lines. Next, we only take
the most relevant cluster into account and focus only on those frequent patterns which lead to the desired
output table similar to Vaarandi [10]. Furthermore, we transform the found frequent patterns and the
output stating the parsed table into a Hidden Markov Model (HMM). We use this HMM as a specific,
however, flexible representation of a pattern for log file parsing to maintain high quality output. After
training our model on one system type and applying it to a different system with slightly different log file
patterns, we achieve an accuracy over 99.99%.
WITH SEMANTICS AND HIDDEN MARKOV MODELS TO AN ADAPTIVE LOG FILE PARSERijnlc
This document presents an adaptive log file parser that uses semantics and hidden Markov models. It first clusters log file lines based on semantics to limit unstructured text. It then builds a hidden Markov model to represent parsing patterns, with log entries as states and extracted values as emissions. When applied to a new system, it adapts the model's transition and emission probabilities to fit the new data. The approach achieves over 99.99% accuracy when trained on one system and applied to another with slightly different log patterns.
EXPLOITING RHETORICAL RELATIONS TO MULTIPLE DOCUMENTS TEXT SUMMARIZATIONIJNSA Journal
Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical relations to address redundancy problem for cluster-based text summarization of multiple documents. We exploited rhetorical relations exist between sentences to group similar sentences into multiple clusters to identify themes of common information. The candidate summary were extracted from these clusters. Then, cluster-based text summarization is performed using Conditional Markov Random Walk Model to measure the saliency scores of the candidate summary. We evaluated our method by measuring the cohesion and separation of the clusters constructed by exploiting rhetorical relations and ROUGE score of generated summaries. The experimental result shows that our method performed well which shows promising potential of applying rhetorical relation in text clustering which benefits text summarization of multiple documents.
Review of Topic Modeling and SummarizationIRJET Journal
This document discusses topic modeling and text summarization techniques. It provides an overview of Latent Dirichlet Allocation (LDA), an algorithm commonly used for topic modeling. LDA can be used to extract keywords from text documents that summarize the document's overall ideas. These keywords can then be used to generate an extractive summary by selecting sentences that reflect the dominant topics. The document reviews several papers on topic modeling, text summarization methods, and approaches that use LDA for multi-document summarization and keyword extraction to generate summaries. It concludes that topic modeling and LDA can help reduce the time needed for summarization by automatically extracting important topics and sentences from documents.
Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical relations to address redundancy problem for cluster-based text summarization of multiple documents. We exploited rhetorical relations exist between sentences to group similar sentences into multiple clusters to identify themes of common information. The candidate summary were extracted from these clusters. Then, cluster-based text summarization is performed using Conditional Markov Random Walk Model to measure the saliency scores of the candidate summary. We evaluated our method by measuring the cohesion and separation of the clusters constructed by exploiting rhetorical relations and ROUGE score of generated summaries. The experimental result shows that our method performed well which shows promising potential of applying rhetorical relation in text clustering which benefits text summarization of multiple documents.
Survey of Machine Learning Techniques in Textual Document ClassificationIOSR Journals
Classification of Text Document points towards associating one or more predefined categories based
on the likelihood expressed by the training set of labeled documents. Many machine learning algorithms plays
an important role in training the system with predefined categories. The importance of Machine learning
approach has felt because of which the study has been taken up for text document classification based on the
statistical event models available. The aim of this paper is to present the important techniques and
methodologies that are employed for text documents classification, at the same time making awareness of some
of the interesting challenges that remain to be solved, focused mainly on text representation and machine
learning techniques.
Text mining efforts to innovate new, previous unknown or hidden data by automatically extracting
collection of information from various written resources. Applying knowledge detection method to
formless text is known as Knowledge Discovery in Text or Text data mining and also called Text Mining.
Most of the techniques used in Text Mining are found on the statistical study of a term either word or
phrase. There are different algorithms in Text mining are used in the previous method. For example
Single-Link Algorithm and Self-Organizing Mapping(SOM) is introduces an approach for visualizing
high-dimensional data and a very useful tool for processing textual data based on Projection method.
Genetic and Sequential algorithms are provide the capability for multiscale representation of datasets and
fast to compute with less CPU time based on the Isolet Reduces subsets in Unsupervised Feature
Selection. We are going to propose the Vector Space Model and Concept based analysis algorithm it will
improve the text clustering quality and a better text clustering result may achieve. We think it is a good
behavior of the proposed algorithm is in terms of toughness and constancy with respect to the formation of
Neural Network.
Machine learning for text document classification-efficient classification ap...IAESIJAI
Numerous alternative methods for text classification have been created because of the increase in the amount of online text information available. The cosine similarity classifier is the most extensively utilized simple and efficient approach. It improves text classification performance. It is combined with estimated values provided by conventional classifiers such as Multinomial Naive Bayesian (MNB). Consequently, combining the similarity between a test document and a category with the estimated value for the category enhances the performance of the classifier. This approach provides a text document categorization method that is both efficient and effective. In addition, methods for determining the proper relationship between a set of words in a document and its document categorization is also obtained.
IRJET- Automatic Recapitulation of Text DocumentIRJET Journal
The document describes an approach for automatically generating summaries of text documents. It involves preprocessing the input text by tokenizing, removing stop words, and stemming words. It then extracts features from the preprocessed text, such as term frequency-inverse document frequency (tf-idf) values. Sentences are scored based on these features and sentences with higher scores are selected to form the summary. Keywords from the text are also identified using WordNet to help select the most relevant sentences for the summary. The proposed approach aims to generate concise yet meaningful summaries using natural language processing techniques.
This document describes a proposed concept-based mining model that aims to improve document clustering and information retrieval by extracting concepts and semantic relationships rather than just keywords. The model uses natural language processing techniques like part-of-speech tagging and parsing to extract concepts from text. It represents concepts and their relationships in a semantic network and clusters documents based on conceptual similarity rather than term frequency. The model is evaluated using singular value decomposition to increase the precision of key term and phrase extraction.
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDINGkevig
Applying natural language processing-related algorithms is currently a popular project in legal
applications, for instance, document classification of legal documents, contract review and machine
translation. Using the above machine learning algorithms, all need to encode the words in the document in
the form of vectors. The word embedding model is a modern distributed word representation approach and
the most common unsupervised word encoding method. It facilitates subjecting other algorithms and
subsequently performing the downstream tasks of natural language processing vis-à-vis. The most common
and practical approach of accuracy evaluation with the word embedding model uses a benchmark set with
linguistic rules or the relationship between words to perform analogy reasoning via algebraic calculation.
This paper proposes establishing a 1,256 Legal Analogical Reasoning Questions Set (LARQS) from the
2,388 Chinese Codex corpus using five kinds of legal relations, which are then used to evaluate the
accuracy of the Chinese word embedding model. Moreover, we discovered that legal relations might be
ubiquitous in the word embedding model.
LARQS: AN ANALOGICAL REASONING EVALUATION DATASET FOR LEGAL WORD EMBEDDINGkevig
This document describes the development of a new legal word embedding evaluation dataset for Chinese called LARQS (Legal Analogical Reasoning Questions Set). It was created using a corpus of 2,388 Chinese legal documents and contains 1,256 questions evaluating 5 categories of legal relationships. The document discusses word embedding and existing evaluation benchmarks. It then describes how LARQS was created by legal experts and its potential usefulness compared to general-purpose benchmarks for evaluating legal-domain word embeddings.
Oracle Insurance Business Intelligence allows insurers to aggregate policy, claim, and customer data into a single location to gain insights. This provides a complete view of customers and the business. Insurers can identify costs savings, revenue opportunities, and improve processes. The solution includes an insurance-specific data warehouse and analytics application with pre-built dashboards and KPIs.
This document provides instructions for applying for an individual insurance broker's license in Massachusetts. It outlines the requirements, which include answering all application questions accurately and completely, including original exam results and course completion certificates. Applicants must also obtain three endorsements from Massachusetts residents and include the $200 application fee. Non-resident brokers have additional requirements regarding powers of attorney and certificates of good standing from their home state.
This document provides a summary of resources available at the Business & IP Centre relating to the insurance industry. It includes directories of insurance companies and service providers, market research reports, trade magazines, and lists of relevant internet sources. The resources cover topics such as different sectors of the insurance market, company profiles, financial performance, and international insurance trends.
This document summarizes a long term health insurance plan that provides hospital cash benefits, major surgical benefits, and a ULIP (unit-linked insurance plan) component for investment. The plan covers the principal insured, spouse, and children. It provides daily cash benefits for hospitalization, lump sum payouts for major surgeries, and allows withdrawals from the investment portion for medical expenses. The plan offers coverage for various medical conditions and surgical procedures.
This document provides an overview of the rewards of working as a lawyer for the federal government compared to private practice. It highlights that while salaries may be lower, government lawyers have more opportunities for meaningful work and responsibility early in their careers. Benefits such as generous leave, health insurance, retirement plans, and loan repayment assistance can help offset the salary difference. Government lawyers also enjoy shaping public policy, reasonable work-life balance without billable hours pressure, and flexibility to transfer between agencies or locations. Interviews with several current and former government attorneys illustrate these points.
This document provides a summary of insurance options for small business owners. It discusses the Business Owner's Policy (BOP), which combines property and liability coverage into one package. The BOP covers buildings, equipment, inventory, and liability for injuries on the business premises or from products. It also describes required auto and worker's compensation coverage in Wisconsin as well as optional policies for health insurance, flood insurance, and umbrella liability insurance that provides additional protection. The document aims to help small business owners understand their insurance needs.
Questions And Answers On Mediation Question 1 What Is Mediationlegal6
This document provides answers to 17 questions about mediation under the Individuals with Disabilities Education Act (IDEA). It defines mediation as a voluntary process that brings parties together with a neutral third party to confidentially discuss issues and try to reach a binding written agreement. Key differences from due process hearings are noted, such as the voluntary nature of mediation and ability of parties to establish their own ground rules and remedies. The benefits, costs, participants and process of mediation are described.
Questions And Answers On Mediation Question 1 What Is Mediation 1legal6
This document summarizes key information about mediation from questions and answers:
Mediation is a voluntary, confidential process that brings parties together with a neutral third party to resolve disputes regarding a child's special education in a written agreement. It offers an opportunity to resolve issues related to identifying, evaluating, placing or providing a free appropriate public education to a child with a disability. Mediation is available whenever a due process hearing is requested or other disputes arise. While mediation and due process hearings address similar issues, mediation is a more informal process where parties establish their own rules and remedies, while hearings have formal, established procedures and binding decisions. The benefits of mediation include the potential for an efficient and effective resolution between parties.
This document provides an overview of wills and estate planning. It discusses what a will is, the importance of having one, how to make a valid will, and what happens if you die without a will. Some key points covered include:
- A will allows you to specify how you want your property distributed after your death. It is not mandatory but has many benefits.
- You can make a will at any time if you are mentally capable. It should be reviewed after major life changes.
- Wills must be in writing and properly witnessed to be valid. People with complex estates are advised to consult a lawyer to draft their will.
- If you die without a will, intestacy laws will determine how
This document discusses two theories for why legal origin influences financial development:
1) The "political" channel argues that legal traditions differ in the priority they give to private property rights versus state power, and common law traditions better protect private contracting rights and financial development.
2) The "adaptability" channel holds that legal traditions differ in their ability to adapt efficiently to changing economic conditions, and common law systems can more flexibly evolve case-by-case while civil law systems rely more on statutory changes. The document assesses the empirical validity of these two channels influencing the relationship between legal origin and financial development.
Here are the key points about establishing liability in medical negligence cases:
- The plaintiff must prove duty of care, breach of standard of care, harm, and causation. All elements must be established.
- Duty of care arises from the patient-practitioner relationship. Practitioners owe a duty to exercise reasonable care and skill.
- The standard of care is what a reasonable practitioner would do in the same circumstances. Expert evidence is usually needed.
- Harm must be proven, usually through medical records and reports. Compensation covers out-of-pocket costs, pain and suffering.
- Guidelines can help show if the standard of care was met or breached, but are not determinative on their
Does Blair Answer The Question An Analysis Of His Performance Atlegal6
This research proposal seeks to analyze Tony Blair's performance at Prime Minister's Questions (PMQs) since 1997 to determine if he adequately answers questions posed. The study will qualitatively analyze a sample of questions from Conservative and Liberal Democratic leaders to categorize Blair's responses. Data will be collected over time to identify trends in answering patterns, attacked topics, and whether Blair addresses issues raised. The main source is the Hansard record from 1988-present. Statistical analysis will quantify results to evaluate if Blair fails to answer questions as accused.
This document is a worksheet for filling out information to create a legal will. It contains 25 questions to collect all necessary personal and financial details. Required fields are marked with an asterisk. Information provided will be kept confidential and used only to draft the will. Once completed, the form should be saved and sent to the law firm.
1. The document discusses distance legal education (DLE), including various synchronous and asynchronous methods, challenges of implementation, and ways to lower barriers.
2. It provides examples of how DLE is being used, such as for guest lectures, semester-long courses, and student interviews via videoconferencing.
3. The Center for Computer-Assisted Legal Instruction (CALI) is working to promote DLE through resources on their website, presentations, and a new service called ClassCaster that allows faculty to record and share audio lessons.
This document discusses different forms of business organization including sole traders, partnerships, limited companies, cooperatives, franchising, multinational companies, and public sector organizations. It provides definitions and outlines advantages and disadvantages of each form. Key factors that influence the choice of legal form are the number of owners, tax implications, ability to take on risk, and privacy requirements.
The Asset Transaction Legal Land Lease Insurance Production And Accountinglegal6
This document outlines a detailed legal, land, insurance, production, and accounting due diligence checklist for an asset transaction. It lists over 50 individual due diligence tasks across various areas that need to be completed, including reviewing litigation history, permits, contracts, title, lease information, production data, environmental records, accounting records, insurance coverage, and more. The goal is to gather all relevant information on the assets and identify any issues to address in the transaction.
The document summarizes insurance coverage for Optimist International and its affiliated organizations effective May 1, 2008 to May 1, 2009. It provides:
1) $1 million in general liability insurance through New Hampshire Insurance Company for bodily injury, property damage, and other exposures for Optimist organizations in the U.S. and Canada.
2) $1 million in hired and non-owned automobile liability insurance also through New Hampshire Insurance Company for Optimist activities in the U.S. and Canada.
3) $1 million in general liability and non-owned automobile liability insurance outside the U.S. and Canada through ACE USA.
4) $10 million in umbrella liability insurance in excess
Health insurance fraud costs the industry between $30-100 billion per year, raising premiums for all policyholders. It is defined as intentionally deceiving an insurance company to receive benefits, such as claiming expenses not incurred or hiding pre-existing conditions. Providers also commit fraud by overbilling or billing for services not rendered. Recently, fake insurance companies have also targeted consumers by offering low rates but disappearing without paying serious medical claims. To avoid fines or prison, policyholders and providers should be honest in all dealings with insurers.
U.S. Legal Forms, Inc. (USLF) is a privately held Subchapter-S Corporation that operates the largest online database of legal forms in the United States. Founded in 1999 by attorneys Frank D. Edens and Lem Adams, III, USLF maintains over 36,000 state-specific legal forms online at uslegalforms.com to assist legal professionals and the public. The founders previously owned LawNetCom, Inc., which developed several legal websites and resources for Mississippi before they expanded their concept to serve national legal needs through USLF.
Steven R. Kuhn is an attorney based in San Juan Capistrano, California. He received his J.D. from Southwestern University School of Law in 1973 and a B.A. in Economics from UCLA in 1970. He has over 45 years of experience as a trial attorney focusing on personal injury, professional negligence, business, construction defect, and real estate litigation. He is currently the principal at Kuhn & Belz, a four attorney firm. He has successfully handled over 3,000 cases with jury verdicts up to $1.2 million.
HCL Notes und Domino Lizenzkostenreduzierung in der Welt von DLAUpanagenda
Webinar Recording: https://www.panagenda.com/webinars/hcl-notes-und-domino-lizenzkostenreduzierung-in-der-welt-von-dlau/
DLAU und die Lizenzen nach dem CCB- und CCX-Modell sind für viele in der HCL-Community seit letztem Jahr ein heißes Thema. Als Notes- oder Domino-Kunde haben Sie vielleicht mit unerwartet hohen Benutzerzahlen und Lizenzgebühren zu kämpfen. Sie fragen sich vielleicht, wie diese neue Art der Lizenzierung funktioniert und welchen Nutzen sie Ihnen bringt. Vor allem wollen Sie sicherlich Ihr Budget einhalten und Kosten sparen, wo immer möglich. Das verstehen wir und wir möchten Ihnen dabei helfen!
Wir erklären Ihnen, wie Sie häufige Konfigurationsprobleme lösen können, die dazu führen können, dass mehr Benutzer gezählt werden als nötig, und wie Sie überflüssige oder ungenutzte Konten identifizieren und entfernen können, um Geld zu sparen. Es gibt auch einige Ansätze, die zu unnötigen Ausgaben führen können, z. B. wenn ein Personendokument anstelle eines Mail-Ins für geteilte Mailboxen verwendet wird. Wir zeigen Ihnen solche Fälle und deren Lösungen. Und natürlich erklären wir Ihnen das neue Lizenzmodell.
Nehmen Sie an diesem Webinar teil, bei dem HCL-Ambassador Marc Thomas und Gastredner Franz Walder Ihnen diese neue Welt näherbringen. Es vermittelt Ihnen die Tools und das Know-how, um den Überblick zu bewahren. Sie werden in der Lage sein, Ihre Kosten durch eine optimierte Domino-Konfiguration zu reduzieren und auch in Zukunft gering zu halten.
Diese Themen werden behandelt
- Reduzierung der Lizenzkosten durch Auffinden und Beheben von Fehlkonfigurationen und überflüssigen Konten
- Wie funktionieren CCB- und CCX-Lizenzen wirklich?
- Verstehen des DLAU-Tools und wie man es am besten nutzt
- Tipps für häufige Problembereiche, wie z. B. Team-Postfächer, Funktions-/Testbenutzer usw.
- Praxisbeispiele und Best Practices zum sofortigen Umsetzen
How to Interpret Trends in the Kalyan Rajdhani Mix Chart.pdfChart Kalyan
A Mix Chart displays historical data of numbers in a graphical or tabular form. The Kalyan Rajdhani Mix Chart specifically shows the results of a sequence of numbers over different periods.
5th LF Energy Power Grid Model Meet-up SlidesDanBrown980551
5th Power Grid Model Meet-up
It is with great pleasure that we extend to you an invitation to the 5th Power Grid Model Meet-up, scheduled for 6th June 2024. This event will adopt a hybrid format, allowing participants to join us either through an online Mircosoft Teams session or in person at TU/e located at Den Dolech 2, Eindhoven, Netherlands. The meet-up will be hosted by Eindhoven University of Technology (TU/e), a research university specializing in engineering science & technology.
Power Grid Model
The global energy transition is placing new and unprecedented demands on Distribution System Operators (DSOs). Alongside upgrades to grid capacity, processes such as digitization, capacity optimization, and congestion management are becoming vital for delivering reliable services.
Power Grid Model is an open source project from Linux Foundation Energy and provides a calculation engine that is increasingly essential for DSOs. It offers a standards-based foundation enabling real-time power systems analysis, simulations of electrical power grids, and sophisticated what-if analysis. In addition, it enables in-depth studies and analysis of the electrical power grid’s behavior and performance. This comprehensive model incorporates essential factors such as power generation capacity, electrical losses, voltage levels, power flows, and system stability.
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Legal Document
1. Improving Legal Document Summarization
Using Graphical Models
M. Saravanana,1, B. Ravindran and S. Raman
Department of Computer Science and Engineering,
IIT Madras, Chennai- 600 036, Tamil Nadu, India.
Abstract. In this paper, we propose a novel idea for applying probabilistic graphical models for
automatic text summarization task related to a legal domain. Identification of rhetorical roles present
in the sentences of a legal document is the important text mining process involved in this task. A
Conditional Random Field (CRF) is applied to segment a given legal document into seven labeled
components and each label represents the appropriate rhetorical roles. Feature sets with varying
characteristics are employed in order to provide significant improvements in CRFs performance. Our
system is then enriched by the application of a term distribution model with structured domain
knowledge to extract key sentences related to rhetorical categories. The final structured summary has
been observed to be closest to 80% accuracy level to the ideal summary generated by experts in the
area.
Keywords. Automatic Text Summarization, Legal domain, CRF model, Rhetorical roles.
1. Introduction
Text summarization is one of the relevant problems in the information era, and it needs to be solved
given that there is exponential growth of data. It addresses the problem of selecting the most important
portions of text and that of generating coherent summaries [1]. The phenomenal growth of legal
documents creates an invariably insurmountable scenario to read and digest all the information.
Automatic summarization plays a decisive role in this type of extraction problems. Manual
summarization can be considered as a form of information selection using an unconstrained
vocabulary with no artificial linguistic limitations [2]. Automatic summarization on the other hand
focuses on the retrieval of relevant sentences of the original text. The retrieved sentences can then be
used as the basis of summaries with the aid of post mining tools. The graphical models were recently
considered as the best machine learning techniques to sort out information extraction issues. Machine
learning techniques are used in Information Extraction tasks to find out typical patterns of texts with
suitable training and learning methodologies. These techniques make the machine learn through
training examples, domain knowledge and indicators.
The work on information extraction from legal documents has largely been based on semantic
processing of legal texts [2] and applying machine learning algorithms like C4.5, Naïve Bayes,
Winnow and SVM’s [3]. These algorithms run on features like cue phrases, location, entities, sentence
length, quotations and thematic words. For this process, Named Entity Recognition rules have been
written by hand for all domain related documents. Also the above studies mention the need for an
active learning method to automate and reduce the time taken for this process. The recent work on
automatically extracting titles from general documents using machine learning methods shows that
machine learning approach can work significantly better than the baseline methods for meta-data
extraction [4]. Some of the other works in the area of legal domain concerns Information Retrieval
and the computation of simple features such as word frequency, cue words and understanding minimal
semantic relation between the terms in a document. Understanding discourse structures and the
linguistic cues in legal texts are very valuable techniques for information extraction systems [5]. For
automatic summarization task, it is necessary to explore more features which are representative of the
characteristics of texts in general and legal text in particular.
Moreover, the application of graphical models to explore the document structure for segmentation
is one of the interesting problems to be tried out. The graphical models have been used to represent
the joint probability distribution p(x, y), where the variables y represent attributes of the entities that
we wish to predict, and the input variables x represent our observed knowledge about the entities. But
__________________________
a
Corresponding Author: Research Scholar, Department of Computer Science & Engineering, IIT Madras, Chennai –
600036, Tamil Nadu, India; Email: msdess@yahoo.com, msn@cse.iitm.ernet.in.
1
Working as a Faculty in Department of Information Technology, Jerusalem College of Engineering, Pallikkaranai,
Chennai-601302, Tamil Nadu, India.
2. modeling the joint distribution can lead to difficulties when using the rich local features that can occur
in text data, because it requires modeling the distribution p(x), which can include complex
dependencies. Modeling these dependencies among inputs can lead to intractable models, but ignoring
them can lead to reduced performance. A solution to this problem is to directly model the conditional
distribution p(y/x), which is sufficient for segmentation. The conditional nature of models such as
McCallum et al's [6] maximum entropy Markov Models result in improved performance on a variety
of text processing tasks. These non-generative finite state models are susceptible to a weakness known
as the label bias problem [7]. The label bias problem can significantly undermine the benefits of using
a conditional model to label sequences. To overcome this problem, Lafferty et al [7] introduced
conditional random fields (CRFs), a form of undirected graphical model that defines a single log-
linear distribution over the joint probability of an entire label sequence given a particular observation
sequence. Conditional Random Field (CRF) is one of the newly used graphical techniques which have
been applied for text segmentation task to explore the set of labels in a given text.
In this paper, we have tried a novel method of applying CRF’s for segmentation of texts in legal
domain and use this knowledge for setting up importance of extraction of sentences through the term
distribution model [8] used for summarization of a document. In many summarizers term weighting
techniques are applied to calculate a set of frequencies and weights based on the number of occurrence
of words. The use of mere term-weighting technique has generally tended to ignore the importance of
term characterization in a document. Moreover, they are not specific in assessing the likelihood of a
certain number of occurrences of particular word in a document and they are not directly derived from
any mathematical model of term distribution or relevancy [9]. The term distribution model used in our
approach is to assign probabilistic weights and normalize the occurrence of the terms in such a way to
select important sentences from a legal document. The ideal sentences are presented in the form of a
structured summary for easy readability and coherency. Also, a comprehensive automatic annotation
has been performed in the earlier stages, which may overcome the difficulties of coherency faced by
other summarizers. Our annotation model has been framed with domain knowledge and is based on
the report of genre analysis [10] for legal documents. The summary generated by our summarizer can
be evaluated with human generated head notes (a short summary for a document generated by experts
in the field) which are available with all legal judgments. The major difficulty of head notes
generated by legal experts is that they are not structured and as such lack the overall details of a
document. To overcome this issue, we come out with a detailed structural summary of a legal
document. We have followed an intrinsic evaluation procedure to compare the sentences present in
structural summary with sentences appearing in human generated head notes.
In this paper, our investigation process deals with three issues which did not seem to have been
examined previously. They are:
1. To apply CRFs for segmentation by the way of structuring a given legal document.
2. To find out whether extracted labels can improve document summarization process.
3. To create generic structure for summary of a legal judgment belonging to different sub-domains.
Experimental results indicate that our approach works well for automatic text summarization of
legal documents. The rest of the paper is organized as follows. In Section 2, we identify the rhetorical
roles present in legal judgment and in Section 3, we explain the details of CRF and discuss the
characteristics of feature sets in Section 4. In Section 5, we describe the proposed system
components. Section 6 gives our experimental results and we make concluding remarks in Section 7.
2. Identification of Rhetorical Roles
In our work, we are in the process of developing a fully automatic summarization system for a legal
domain on the basis of Lafferty’s [7] segmentation task and Teufel & Moen’s [11] gold standard
approaches. Legal judgments are different in characteristics compared with articles reporting scientific
research papers and other simple domains related to the identification of basic structure of a
document. The main idea behind our approach is to apply probabilistic models for text segmentation
and to identify the sentences which are more important to the summary and that might be presented as
labeled text tiles. Useful features might include standard IR measures such as probabilistic feature of
word, but other highly informative features are likely to refer conditional relatedness of the sentences.
We also found the usefulness of other range of features in determining the rhetorical status of
sentences present in a document.
For a legal domain, the communication goal of each judge is to convince his/her peers with their
sound context and having considered the case with regard to all relevant points of law. The
fundamental need of a legal judgment is to legitimize a decision from authoritative sources of law. To
perform a summarization methodology and find out important portions of a legal document is a
3. complex problem. Even the skilled lawyers are facing difficulty in identifying the main decision part
of a law report. The genre structure identified in our process plays a crucial role in identifying the
main decision part in the way of breaking the document in anaphoric chains. A different set of
contextual rhetorical schemes will be taken into considerations which are discussed here with different
purview. Table 1 shows the basic rhetorical roles present in a legal document based on Teufel &
Moen’s [11] and Farzindar [12].
Table 1. Description of the basic rhetorical scheme for a legal domain.
Labels Description
Facts of the The sentence describes the details of the case
case
Background The sentence contains the generally accepted background knowledge (i.e., legal details,
summary of law, history of a case)
Own The sentence contains statements that can be attributed to the way judges conduct the case.
Case The sentences contain the details of other cases coded in this case.
relatedness
These general classifications have been enhanced into seven different labeled elements in our
work for a more structured presentation. Our current version of labels will explore the different
category of contents present in the legal document and this in turn will be helpful at the time of
presentation of a final summary. To identify the labels, we need to create a rich collection of features,
which includes all important features like concept and cue phrase identification, structure
identification, abbreviated words, length of a word, position of sentences, etc,. The position in the
text or within the section does not appear to be significant for any Indian law judgments, since most of
the judgments do not follow any standard format of discussion related to the case. Some of the
judgments do not even follow the general structure of a legal document. To overcome this problem,
positioning of word or sentence in a document is not considered as one of the important features in our
work. Our approach to explore the elements from the structure of legal documents has been
generalized in the following fixed set of seven rhetorical categories based on Bhatia’s [10] genre
analysis shown in Table 2.
In common law system, decisions made by judges are important sources of applications and
interpretations of law. A judge generally follows the reasoning used by earlier judges in similar cases.
This reasoning is known as the reason for the decision (Ratio decidendi). The important portion of a
head note includes the sentences which are related to the reason for the decision. These sentences very
well justify the judge’s decision, and in non-legal terms may describe the central generic sentences of
the text. So, we reckon this as one of the important elements to be included in our genre structure of
judgments. Usually, the knowledge of the ratio appears in decision section, but sometimes may appear
in the earlier portion of a document. In our approach, we have given importance to the cues for the
identification of the central generic sentence in a law report rather than the position of text. From the
Indian court judgments, we found that ratio can be found in any part of the decision section of a law
report, but usually they appear as complex sentences. It is not uncommon to find that the experts differ
among themselves on the identification of ratio of the decision in a given judgment. This shows the
complexity of the task.
Exploration of text data is a complex proposition. But in general, we can figure out two
characteristics from the text data; the first one is the statistical dependencies that exist between the
entities related to the proposed model, and the second one is the cue phrase / term which can field a
rich set of features that may aid classification or segmentation of given document. More details of
CRFs are given in the next section.
4. Table 2. The current working version of the rhetorical annotation scheme for
legal judgments.
Rhetorical Status Description
Identifying the case The sentences that are present in a judgment to identify the issues to be
decided for a case. Courts call them as “Framing the issues”.
Establishing facts of the The facts that are relevant to the present proceedings/litigations that stand
case proved, disproved or unproved for proper applications of correct legal
principle/law.
Arguing the case Application of legal principle/law advocated by contending parties to a given
set of proved facts.
History of the case Chronology of events with factual details that led to the present case between
parties named therein before the court on which the judgment is delivered.
Arguments (Analysis) The court discussion on the law that is applicable to the set of proved facts by
weighing the arguments of contending parties with reference to the statute
and precedents that are available..
Ratio decidendi Applying the correct law to a set of facts is the duty of any court. The reason
(Ratio of the decision) given for application of any legal principle/law to decide a case is called
Ratio decidendi in legal parlance. It can also be described as the central
generic reference of text.
Final decision It is an ultimate decision or conclusion of the court following as a natural or
(Disposal) logical outcome of ratio of the decision
3. Conditional Random Fields
Conditional Random Fields (CRFs) model is one of the recently emerging graphical models which has
been used for text segmentation problems and proved to be one of the best available frame works
compared to other existing models [7]. In general, the documents involved are segmented and each
segment gets one label value might be considered as a needed functionality for text processing which
could be addressed by many semantic and rule based methods. Recently, machine learning tools are
extensively used for this purpose. CRFs are undirected graphical models used to specify the
conditional probabilities of possible label sequences given an observation sequence. Moreover, the
conditional probabilities of label sequences can depend on arbitrary, non independent features of the
observation sequence, since we are not forming the model to consider the distribution of those
dependencies. These properties led us to prefer CRFs over HMM and SVM classifier [13]. In a special
case in which the output nodes of the graphical model are linked by edges in a linear chain, CRFs
make a first-order Markov independence assumption with binary feature functions, and thus can be
understood as conditionally-trained finite state machines (FSMs) which are suitable for sequence
labeling.
Let us define the linear chain CRF with parameters C = {C1,C2,…..} defining a conditional
probability for a label sequence l= l1,…..lw (e.g., Establishing facts of the case, Final decision, etc.)
given an observed input sequence s = s1,…sW to be
1 wm
PC(l | s) = --- exp[ ∑∑ Ck fk (lt-1, lt. s, t) ……………. (1)
t=1 k=1
Zs
where Zs is the normalization factor that makes the probability of all state sequences sum to one,
fk (lt-1, lt, s, t) is one of m feature functions which is generally binary valued and Ck is a learned weight
associated with feature function. For example, a feature may have the value of 0 in most cases, but
given the text “points for consideration”, it has the value 1 along the transition where lt-1 corresponds
to a state with the label Identifying the case, lt corresponds to a state with the label History of the
case, and fk is the feature function PHRASE= “points for consideration” is belongs to s at position t in
the sequence. Large positive values for Ck indicate a preference for such an event, while large
negative values make the event unlikely and near zero for relatively uninformative features. These
weights are set to maximize the conditional log likelihood of labeled sequence in a training set D = {(
st, lt) : t = 1,2,…w), written as:
LC (D) = ∑log PC(li | si)
i
wm
= ∑ ( ∑ ∑ Ck fk (lt-1, lt. s, t) - log Zsi ) . ……… (2)
i t=1 k=1
5. The training state sequences are fully labeled and definite, the objective function is convex, and
thus the model is guaranteed to find the optimal weight settings in terms of LC (D). The probable
labeling sequence for an input si can be efficiently calculated by dynamic programming using
modified Viterbi algorithm. These implementations of CRFs are done using newly developed java
classes which also uses a quasi-Newton method called L-BFGS to find these feature weights
efficiently.
4. Feature sets
Features common to information retrieval, which were used successfully in the genre of different
domains, will also be applicable to legal documents. The choice of relevant features is always vital to
the performance of any machine learning algorithm. The CRF performance has been improved
significantly by efficient feature mining techniques. Identifying state transitions of CRF were also
considered as one of the important features in any of information extraction task [13].
In addition to the standard set of features, we have also added other related features to reduce the
complexity of legal domain. We will discuss some of the important features which have been included
in our proposed model all in one framework:
Indicator/cue phrases – The term ‘cue phrase’ indicates the key phrases frequently used which
are the indicators of common rhetorical roles of the sentences (e.g. phrases such as “We agree with
court”, “Question for consideration is”, etc.,). Most of the earlier studies dealt with the building of
hand-crafted lexicons where each and every cue phrases related to different labels. In this study, we
encoded this information and generated automatically explicit linguistic features. Our initial cue
phrase set has been enhanced based on expert suggestions. These cue phrases can be used by the
automatic summarization system to locate the sentences which correspond to a particular category of
genre of legal domain. If training sequence contains “No provision in ….act/statute”, “we hold”, “we
find no merits” all labeled with RATIO DECIDENDI, the model learns that these phrases are
indicative of ratios, but cannot capture the fact that all phrases are present in a document. But the
model faces difficulty in setting the weights for the feature when the cues appear within quoted
paragraphs. This sort of structural knowledge can be provided in the form of rules. Feature functions
for the rules are set to 1 if they match words/phrases in the input sequence exactly.
Named entity recognition - This type of recognition is not considered fully in summarizing
scientific articles [10]. But in our work, we recognize a wide range of named entities and generate
binary-valued entity type features which take the value 0 or 1 indicating the presence or absence of a
particular entity type in the sentences.
Local features and Layout features - One of the main advantages of CRFs is that they easily
afford the use of arbitrary features of the input. One can encode abbreviated features; layout features
such as position of paragraph beginning, as well as the sentences appearing with quotes, all in one
framework. We look at all these features in our legal document extraction problem, evaluate their
individual contributions, and develop some standard guidelines including a good set of features.
State Transition features - In CRFs, state transitions are also represented as features [13]. The
feature function fk (lt-1, lt. s, t) in Eq. (1) is a general function over states and observations. Different
state transition features can be defined to form different Markov-order structures. We define state
transition features corresponding to appearance of years attached with Section and Act Nos. related to
the labels Arguing the case and Arguments. Also the appearance of some of the cue phrases in a
label identifying the case can be considered to Arguments when they appear within quotes. In the
same way, many of the transition features have been added in our model. Here inputs are examined in
the context of the current and previous states. Moreover, in some cases, we need to add a set of
previous sentences based on the states.
Legal vocabulary features - One of the simplest and most obvious features set is decided using
the basic vocabularies from a training data. The words that appear with capitalizations, affixes, and in
abbreviated texts are considered as important features. Some of the phrases that include v. and
act/section are the salient features for Arguing the case and Arguments categories.
5. The Proposed System
The overall architecture is shown in Figure 1. It consists of different modules organized as a channel
for text summarization task.
6. Legal Feature set
document
Segmented
Pre- CRF model
text with
processing labels
Post Summary with
Mining ratio & final
Term decision
distribution
model
Figure 1. Different processing stages of a system
The automatic summarization process starts with sending legal document to a preprocessing
stage. In this preprocessing stage, the document is to be divided into segments, sentences and tokens.
We have introduced some of the new feature identification techniques to explore paragraph
alignments. This process includes the understanding of abbreviated texts and section numbers and
arguments which are very specific to the structure of legal documents. The other useful statistical
natural language processing tools, such as filtering out stop list words, stemming etc., are carried out
in the preprocessing stage. The resulting intelligible words are useful in the normalization of terms in
the term distribution model. The other phase dealing with selecting suitable feature sets for the
identification of rhetorical status of each sentence has been implemented with a graphical model
(CRFs) which aid in document segmentation.
The term distribution model used in our architecture is K-mixture model [14]. The K-mixture
model is a fairly good approximation model compared to Poisson model and it is described as the
mixture of Poisson distribution and its terms can be computed by varying the Poisson parameters
between observations. The formula used in K-mixture model for the calculations of the probability of
the word wi appearing k times in a document is given as:
Pi(k) = (1-r) δk,0 + r _(s) k ……..…. (3)
k
s +1 (s+1)
where δk,0 = 1 if and only if k = 0, and δk,0 = 0 otherwise. The variables r and s are parameters
that can be fit using the observed mean (t) and observed Inverse Document Frequency (IDF). IDF is
not usually considered as an indicator of variability, though it may have certain advantages over
variance. The parameter r used in the formula refers to the absolute frequency of the term, and s used
to calculate the number of “extra terms” per document in which the term occurs (compared to the case
where a term has only one occurrence per document). The most frequently occurring words in all
selected documents are removed by using the measure of IDF that is used to normalize the occurrence
of words in the document. In this K-mixture model, each occurrence of a content word in a text
decreases the probability of finding an additional term, but the decrease becomes consecutively
smaller. Hence the application of K-mixture model brings out a good extract of generic sentences
from a legal document to generate a summary. Post mining stage deals with matching of sentences
present in the summary with segmented document to evolve the structured summary.
The sentences related to the labels which have been selected during term distribution model are
useful to present the summary in the form of structured way. This structured summary is more useful
for generating coherency and readability among the sentences present in the summary. Legal
judgment head notes are mainly concentrated on the label Ratio of Decision. So, in addition to our
structured summary, we are also giving ratio decidendi and final decision of a case. This form of
summary of legal document is more useful to the legal community not only for the experts but also for
the practicing lawyers.
7. 6. Results and Discussion
Our corpus presently consists of 200 legal documents related to rent control act, out of which 50 were
annotated. It is a part of a larger corpus of 1000 documents in different sub-domains of civil court
judgments which we collected from Kerala lawyer archive (www.keralalawyer.com). Each document
in a corpus contains an average of 20 to 25 words in a sentence. The entire corpus consists of
judgments dated up to the year 2006. The judgments can be divided into exclusive sections like Rent
Control, Motor Vehicle, Family Law, Patent, Trademark and Company law, Taxation, Sales Tax,
Property and Cyber Law, etc. Even though it is unique, we had a generalized methodology of
segmentation for document belonging to different categories of civil court judgments. The header of a
legal judgment contains the information related to a petitioner, respondent, judge details, court name
and case numbers which were removed and stored in a separate header dataset. It is a common
practice to consider human performance as an upper bound for most of the IR tasks. So in our
evaluation, the performance of the system has been successfully tested by matching with human
annotated documents.
We evaluate our work in two steps; first the evaluation of correct segmentation of the legal
judgments and second, a macro evaluation of a final summary. The evaluation of the first step looks
very promising since we have obtained more than 90% correct segmentation in most of the categories
(which included the most important Framing of Issues and Ratio Decidendi) and nearer to 80% in
Arguments and Arguing the case rhetorical schemes shown in Table 3. Since, we have followed
intrinsic measure of evaluation as an evaluation procedure we need to establish the performance of
annotation done by two different annotators, with the help of Kappa Coefficient [15]. The advantage
of Kappa over annotation scheme is that it factors out random agreement among the categories. The
experimental results shows that humans identify the seven rhetorical categories with a reproducibility
of K = 0.73 (N=3816; k=2, where K stands for the Kappa coefficient, N for the number of sentences
annotated and k for the number of annotators). Now, we report the system performance with precision
and recall values for all seven rhetorical categories using CRF model in Table 3. The system performs
well for Ratio decidendi and final decision which are the main contents for the head notes generated
by human experts. Identification of the case may not be precisely identifiable from the corpus, but it is
a problem even for human annotators with some of the documents. In our system, to overcome this
difficulty the ratio is rewritten in question format in such cases
Table 3. Precision, Recall and F-measure for seven rhetorical categories.
Rhetorical Category Precision Recall F-Measure
Identifying the case 0.946 0.868 0.905
Establishing facts of the case 0.924 0.886 0.904
Arguing the case 0.824 0.787 0.805
History of the case 0.808 0.796 0.802
Arguments 0.860 0.846 0.853
Ratio decidendi 0.924 0.901 0.912
Final decision 0.986 0.962 0.974
Micro Average 0.896 0.864 0.879
Table 3 shows the good performance of CRF model with efficient features sets for text
segmentation task. The above results may contribute to the generation of structured and efficient
summary in next phase. Use of these identified rhetorical categories can help in modifying the
probabilistic weights of term distribution model in such way as to give more importance to ratio of the
decision and others categories. The extracted key sentences from the legal document using
probabilistic model should be compared with head notes generated by experts in the area. Figure 2
shows the results of our system generated summary in unstructured format, but it uses CRF results for
important sentence extraction. The result of system generated summary is compared with human
generated head notes, and it is found that F-measure reaches approximately 80%. We need to improve
more on the findings of ratio of decision category to get 100% accuracy in segmentation stage, so as
to improve final system-generated summary to maximum level. The summary presented in Figure 3
shows the importance of arranging the sentences in a structured manner as it not only improves the
readability and coherency but also gives out more inputs like court’s arguments to get a
comprehensive view of the Ratio and Disposal of the case.
8. Landlord is the revision petitioner. Evictions was sought for under sections 11 (2) (b) and 11 (3) of the Kerala buildings
lease and rent control act, 1965. Evidence would indicate that petitioners mother has got several vacant shop buildings
of her own. Admittedly the building was rented out by the rent agreement dated 6.10.1980 by the mother of the
petitioner. An allegation had been made that in reality there was no sale and the sale deed was a paper transaction. We
find force in the contention of the counsel appearing for the tenant. The court had to record a finding on this point. This
is a case where notice of eviction was sent by the mother of the petitioner which was replied by the tenant by Ext. B2
dated 26.1.1989. The landlady was convinced that she could not successively prosecute a petition for eviction and hence
she gifted the tenanted premises to her son. On facts we are convinced that Ext. A1 gift deed is a sham document, as
stated by the tenant, created only to evict the tenant. We are therefore of the view that the appellate authority has
properly exercised the jurisdiction and found that there is no bonafide in the claim. We therefore confirm the order of
the appellate authority and reject the revision petition. The revision petition is accordingly dismissed.
Figure 2. Unstructured summary produced by our system, the original judgment has
1100 words and summary is 15% of the source.
(Before K. S. Radhakrishnan & J. M. James, JJ)- Thursday, the 10th October 2002/ 18th Asvina, 1924 - CRP. No. 1675
of 1997(A)
Petitioner : Joseph - Respondent: George K. - Court : Kerala High Court
Rhetorical Status Relevant sentences
Identifying the case The appellate authority has properly exercised the jurisdiction and found that there is
no bonafide in the claim – Is it correct?.
Establishing the facts of the We find force in the contention of the counsel appearing for the tenant.
case This is a case where notice of eviction was sent by the mother of the petitioner which
was replied by the tenant by Ext. B2 dated 26.1.1989. The landlady was convinced that
she could not successively prosecute a petition for eviction and hence she gifted the
tenanted premises to her son..
Arguments Apex court held as follows:
quot;The appellate authority rejected the tenant's case on the view that tenant could not
challenge the validity of the sale deed executed in favour of Mohan Lal because the
tenant was not a party to it. We do not think this was a correct view to take. An
allegation had been made that in reality there was no sale and the sale deed was a paper
transaction. The court had to record a finding on this point. The appellate authority
however did not permit counsel for the tenant to refer to evidence adduced on this
aspect of the matter. The High Court also did not advert to it. We, therefore, allow this
appeal set aside the decree for eviction and remit the case to the trial court to record a
finding on the question whether the sale of the building to respondent Mohan Lal was a
bonafide transaction upon the evidence on record.quot;
Ratio of the decision We are therefore of the view that the appellate authority has properly exercised the
jurisdiction and found that there is no bonafide in the claim.
Final decision We therefore confirm the order of the appellate authority and reject the revision
petition. The revision petition is accordingly dismissed.
Figure 3. System output (Structured summary) for example judgment containing title, petitioner, respondent,
rhetorical categories and relevant sentences.
7. Conclusion
This paper highlights the construction of proper features sets with an efficient use of CRF for
segmentation and presentation tasks, in the application of extraction of key sentences from legal
judgments. While the system presented here shows the improvement in results, there is still much to
be explored. The segmentation of a document based on genre analysis is an added advantage and this
could be used for improving the results during the extraction of key sentences by applying term
distribution model. The mathematical model based approach for extraction of key sentences has
yielded a better results compared to simple term weighting methods. We have also applied better
evaluation metrics to evaluate the results rather than using simple word frequency and accuracy.
We have presented an initial annotation scheme for the rhetorical structure of the rental act sub-
domain, assigning a label indicating the rhetorical status of each sentence in a portion of a document.
The next phase of our research work will involve refining our annotation scheme for other sub-
domains related to legal domain. After completing this phase we plan to develop a legal ontology for
querying and generating multi-document summarization in the legal domain.
Acknowledgements. We would like to thank the legal fraternity for the assistance and guidance
governs to me. Especially we express my sincere gratitude to Mr. S. Mohan, a retired chief justice of
Supreme Court of India for his expert opinions. We also thank advocates Mr. S.B.C. Karunakaran and
Mr. K.N. Somasundaram for their domain advice and continuous guidance in understanding the
structure of legal document and for hand annotated legal documents.
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