Introduction
Algorithms
Soundex
Difference
Levenshtein distance
Model of applying fuzzy logic in searching
Word index
Fuzzy logic algorithm in searching
Further development
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
This document discusses ontology mapping. It begins with an introduction to the semantic web and ontologies. Ontology mapping is important for allowing different ontologies to be aligned and related. There are different types of ontology mapping including alignment, merging, and mapping. The document then surveys some popular ontology mapping techniques including GLUE, PROMPT, and QOM. It evaluates these techniques and discusses their inputs, outputs, and approaches. The document concludes that semantic web research is important for advancing web technologies and realizing the goals of web 3.0. Future work could involve developing new ontology mapping techniques and publishing research on existing mapping methods.
The document describes Cornell Notes, an academic note-taking system involving recording information, summarizing, reciting from memory, and reviewing notes. It then discusses an application called Cornell Notes Application that allows users to take and organize Cornell notes digitally using technologies like jQuery, TinyMCE, and XML. The application has some technical issues to address regarding compatibility between libraries and loading problems.
Suvremeni informacijsko komunikacijski sustavi za usmjeravanje prometnih enti...Sberbank d.d.
Primjena suvremenih informacijsko komunikacijskih sustava, poput Cloud Computing platforme, u svrhu pružanja usluge usmjeravanja prometnih entiteta u stalnom je razvoju. Danas je dostupno tek nekolicina komercijalnih usluga koje su slabo primjenjive u svakodnevnoj uporabi. U svrhu usmjeravanja još uvijek se pretežno koriste samostalni navigacijski uređaji. Rad daje pregled razvoja i primjene suvremenih informacijsko komunikacijskih sustava i usluga primjenjivih za usmjeravanje prometnih entiteta. Uz navedeno, u radu je dana komparativna analiza primjene trenutno dostupnih Cloud Computing baziranih usluga za usmjeravanje prometnih entiteta. Provedeno istraživanje temelje je za daljnju analizu mogućnosti primjene XaaS modela te novih vrijednosnih lanaca u isporuci usluga usmjeravanja prometnih entiteta.
ACTIVE NOISE CANCELLATION IN A LABORATORY DUCT USING FUZZY LOGIC AND NEURAL ...Rishikesh .
The main goal of this paper is to present a simulation scheme to simulate an adaptive filter using LMS (Least mean square) adaptive algorithm for noise cancellation. The main objective of the noise cancellation is to estimate the noise signal and to subtract it from original input signal plus noise signal and hence to obtain the noise free signal. There is an alternative method called adaptive noise cancellation for estimating a speech signal corrupted by an additive noise or interference. This method uses a primary input signal that contains the speech signal and a reference input containing noise. The reference input is adaptively filtered and subtracted from the primary input signal to obtain the estimated signal. In this method the desired signal corrupted by an additive noise can be recovered by an adaptive noise canceller using LMS (least mean square) algorithm. This adaptive noise canceller is useful to improve the S/N ratio. Here we estimate the adaptive filter using Labview /MATLAB/SIMULINK environment . For achieving the goal we also use modern algorithms like ANFIS, FIS and Neural Network and compare the PSD of all the algorithms.
Security Audit and Mechanism of Protecting e-Learning System at the Faculty o...Sberbank d.d.
Analysis of FPZ LMS system application
Security auditing methods
Methodology of FPZ LMS system protection
Preliminary protection
Database protection
Protection within web application
Implemented LMS protection against the most common forms of attacks
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information
Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology
is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic
concepts that characterizes the domain as well as their definitions and interrelationships. This paper will
describe some algorithms for identifying semantic relations and constructing an Information Technology
Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed
based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our
algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language
Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences.
We then extract these sentences based on English pattern in order to build training set. We use a
random sample among 245 categories of ACM to evaluate our results. Results generated show that our
system yields superior performance.
This document discusses ontology mapping. It begins with an introduction to the semantic web and ontologies. Ontology mapping is important for allowing different ontologies to be aligned and related. There are different types of ontology mapping including alignment, merging, and mapping. The document then surveys some popular ontology mapping techniques including GLUE, PROMPT, and QOM. It evaluates these techniques and discusses their inputs, outputs, and approaches. The document concludes that semantic web research is important for advancing web technologies and realizing the goals of web 3.0. Future work could involve developing new ontology mapping techniques and publishing research on existing mapping methods.
The document describes Cornell Notes, an academic note-taking system involving recording information, summarizing, reciting from memory, and reviewing notes. It then discusses an application called Cornell Notes Application that allows users to take and organize Cornell notes digitally using technologies like jQuery, TinyMCE, and XML. The application has some technical issues to address regarding compatibility between libraries and loading problems.
Suvremeni informacijsko komunikacijski sustavi za usmjeravanje prometnih enti...Sberbank d.d.
Primjena suvremenih informacijsko komunikacijskih sustava, poput Cloud Computing platforme, u svrhu pružanja usluge usmjeravanja prometnih entiteta u stalnom je razvoju. Danas je dostupno tek nekolicina komercijalnih usluga koje su slabo primjenjive u svakodnevnoj uporabi. U svrhu usmjeravanja još uvijek se pretežno koriste samostalni navigacijski uređaji. Rad daje pregled razvoja i primjene suvremenih informacijsko komunikacijskih sustava i usluga primjenjivih za usmjeravanje prometnih entiteta. Uz navedeno, u radu je dana komparativna analiza primjene trenutno dostupnih Cloud Computing baziranih usluga za usmjeravanje prometnih entiteta. Provedeno istraživanje temelje je za daljnju analizu mogućnosti primjene XaaS modela te novih vrijednosnih lanaca u isporuci usluga usmjeravanja prometnih entiteta.
ACTIVE NOISE CANCELLATION IN A LABORATORY DUCT USING FUZZY LOGIC AND NEURAL ...Rishikesh .
The main goal of this paper is to present a simulation scheme to simulate an adaptive filter using LMS (Least mean square) adaptive algorithm for noise cancellation. The main objective of the noise cancellation is to estimate the noise signal and to subtract it from original input signal plus noise signal and hence to obtain the noise free signal. There is an alternative method called adaptive noise cancellation for estimating a speech signal corrupted by an additive noise or interference. This method uses a primary input signal that contains the speech signal and a reference input containing noise. The reference input is adaptively filtered and subtracted from the primary input signal to obtain the estimated signal. In this method the desired signal corrupted by an additive noise can be recovered by an adaptive noise canceller using LMS (least mean square) algorithm. This adaptive noise canceller is useful to improve the S/N ratio. Here we estimate the adaptive filter using Labview /MATLAB/SIMULINK environment . For achieving the goal we also use modern algorithms like ANFIS, FIS and Neural Network and compare the PSD of all the algorithms.
Security Audit and Mechanism of Protecting e-Learning System at the Faculty o...Sberbank d.d.
Analysis of FPZ LMS system application
Security auditing methods
Methodology of FPZ LMS system protection
Preliminary protection
Database protection
Protection within web application
Implemented LMS protection against the most common forms of attacks
Machine translation from English to HindiRajat Jain
Machine translation a part of natural language processing.The algorithm suggested is word based algorithm.We have done Translation from English to Hindi
submitted by
Garvita Sharma,10103467,B3
Rajat Jain,10103571,B6
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATAijistjournal
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. This paper will describe some algorithms for identifying semantic relations and constructing an Information Technology Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences. We then extract these sentences based on English pattern in order to build training set. We use a random sample among 245 categories of ACM to evaluate our results. Results generated show that our system yields superior performance.
From TREC to Watson: is open domain question answering a solved problem?Constantin Orasan
The document summarizes a presentation on question answering systems. It begins by providing context on information overload and defining question answering. It then discusses the evolution of QA systems from early databases to today's open-domain systems. The presentation focuses on IBM's Watson system, providing an overview of its unprecedented ability to answer open-domain questions as well as the massive resources required for its development. It concludes by arguing that open-domain QA remains unsolved and that closed-domain, interactive QA may be more practical for real-world applications.
A Comprehensive Review Of Automated Essay Scoring (AES) Research And DevelopmentAnna Landers
This document provides a comprehensive review of research on Automated Essay Scoring (AES). It discusses three common AES frameworks: Content Similarity, Machine Learning, and Hybrid. It summarizes key AES systems including Project Essay Grader, Intelligent Essay Assessor, IntelliMetric, and e-rater. It also reviews past literature on AES categorized by attribute (style, content, hybrid), methodology, prediction model, and findings. The review aims to analyze the development of AES and provide recommendations for its future.
Pedro is a tool that combines rapid data modelling and ontology services. It renders data entry forms from an XML schema, allowing users to create data files that conform to the data model. Users can mark up text fields with controlled vocabulary terms supplied by ontology services. Originally developed for a proteomics consortium, Pedro has since been used in other domains like genomics and security. It validates data files against the underlying data model and remembers where controlled vocabulary terms came from through its ontology services.
Query Translation for Data Sources with Heterogeneous Content Semantics Jie Bao
The document discusses query translation for data sources with heterogeneous content semantics. It proposes using ontology-extended data sources to make explicit the implicit ontologies associated with data. The key aspects covered include translating queries between different data content ontologies using conversion functions and interoperation constraints to ensure sound, complete, or exact translations.
Semantic Relatedness of Web Resources by XESA - Philipp SchollCROKODIl consortium
This document discusses using extended explicit semantic analysis (XESA) to measure semantic relatedness between short text snippets for recommendation purposes. It proposes enhancing ESA by incorporating additional semantic information from Wikipedia, such as article links and categories. An evaluation compares the performance of ESA, XESA using the article graph, XESA using categories, and a combination. The results show that XESA using the article graph improves over ESA by up to 9% and performs best for recommending related snippets.
French machine reading for question answeringAli Kabbadj
This paper proposes to unlock the main barrier to machine reading and comprehension French natural language texts. This open the way to machine to find to a question a precise answer buried in the mass of unstructured French texts. Or to create a universal French chatbot. Deep learning has produced extremely promising results for various tasks in natural language understanding particularly topic classification, sentiment analysis, question answering, and language translation. But to be effective Deep Learning methods need very large training da-tasets. Until now these technics cannot be actually used for French texts Question Answering (Q&A) applications since there was not a large Q&A training dataset. We produced a large (100 000+) French training Dataset for Q&A by translating and adapting the English SQuAD v1.1 Dataset, a GloVe French word and character embed-ding vectors from Wikipedia French Dump. We trained and evaluated of three different Q&A neural network ar-chitectures in French and carried out a French Q&A models with F1 score around 70%.
Semantic Interoperability - grafi della conoscenzaGiorgia Lodi
This document summarizes Giorgia Lodi's presentation on meaningful data and semantic interoperability in the Italian public sector. Lodi discusses issues with data quality such as missing values, semantics mismatches, and use of strings instead of codes. She argues that adopting semantic web standards like RDF, OWL and SPARQL can help address these issues by linking data together and representing it semantically. Ontologies and knowledge graphs can be used to represent domain knowledge and infer new facts. Tools like FRED can generate knowledge graphs from unstructured text. Overall, Lodi argues that semantic web technologies have the potential to improve data interoperability and quality in the public sector, though challenges remain.
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
Dissertation defense slides on "Semantic Analysis for Improved Multi-document...Quinsulon Israel
This document outlines Quinsulon Israel's Ph.D. dissertation defense on using semantic analysis to improve multi-document summarization. The dissertation examines using semantic triples clustering and semantic class scoring of sentences to generate summaries. It reviews prior work on statistical, features combination, graph-based, multi-level text relationship, and semantic analysis approaches. The dissertation aims to improve the baseline method and evaluate the effects of semantic analysis on focused multi-document summarization performance.
Ontology engineering of automatic text processing methodsIJECEIAES
Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.
The task of keyword extraction is to automatically identify a set of terms that best describe the document. Automatic keyword extraction establishes a foundation for various natural language processing applications: information retrieval, the automatic indexing and classification of documents, automatic summarization and high-level semantic description, etc. Although the keyword extraction applications usually work on single documents (document-oriented task), keyword extraction is also applicable to a more demanding task, i.e. the keyword extraction from a whole collection of documents or from an entire web site, or from tweets from Twitter. In the era of big-data, obtaining an effective and efficient method for automatic keyword extraction from huge amounts of multi-topic textual sources is of high importance.
We proposed a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The selectivity slightly outperforms an extraction based on the standard centrality measures. Therefore, the selectivity and its modification – generalized selectivity as the node centrality measures are included in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network and it can be easily ported to new languages and used in a multilingual scenario. The true potential of the proposed SBKE method is in its generality, portability and low computation costs, which positions it as a strong candidate for preparing collections which lack human annotations for keyword extraction. Testing of the portability of the SBKE was tested on Croatian, Serbian and English texts – more precisely it was developed on Croatian News and ported for extraction from parallel abstracts of scientific publication in the Serbian and English languages.
The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set, the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still, there are drawbacks – the method can extract only the words that appear in the text.
The document discusses using inductive logic programming (ILP) to perform information extraction (IE) on biomedical texts. It outlines applying ILP to learn recursive theories from annotated examples to fill slots in templates and extract entities. The learning strategy involves searching for theories for each concept in parallel before discovering dependencies between concepts. Text processing involves tokenization, POS tagging and domain dictionaries before description generation for ILP.
The document discusses using inductive logic programming (ILP) to perform information extraction (IE) on biomedical texts. It outlines applying ILP to learn recursive theories from annotated examples to fill slots in templates and extract entities. The learning strategy involves searching for theories for each concept in parallel before discovering dependencies between concepts. Text processing involves annotating documents before using features of tokens for ILP learning.
IRJET- Short-Text Semantic Similarity using Glove Word EmbeddingIRJET Journal
The document describes a study that uses GloVe word embeddings to measure semantic similarity between short texts. GloVe is an unsupervised learning algorithm for obtaining vector representations of words. The study trains GloVe word embeddings on a large corpus, then uses the embeddings to encode short texts and calculate their semantic similarity, comparing the accuracy to methods that use Word2Vec embeddings. It aims to show that GloVe embeddings may provide better performance for short text semantic similarity tasks.
IRJET - Automated Essay Grading System using Deep LearningIRJET Journal
This document describes an automated essay grading system that uses deep learning techniques. It discusses how previous grading systems used machine learning algorithms like linear regression and support vector machines. It then presents a new system that uses an LSTM and dense neural network model to grade essays on a scale of 1-10. The system preprocesses essays by removing stopwords and numbers before converting the text to word vectors as input to the deep learning model. It aims to reduce the time spent on grading large numbers of essays compared to manual grading.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The document discusses using machine learning to identify factors that correlate with essay grades and English proficiency levels. It analyzes a database of 481 essays written by Dutch students and coded with 81 linguistic variables. Various machine learning algorithms are tested to predict the essays' proficiency levels as assigned by human raters. The best performing algorithm, Logistic Model Tree, achieves accuracy comparable to human raters when using all 81 features directly. Feature selection and discretization are also tested to identify a minimal set of highly predictive features.
The document discusses two NSF-funded research projects on intelligence and security informatics:
1. A project to filter and monitor message streams to detect "new events" and changes in topics or activity levels. It describes the technical challenges and components of automatic message processing.
2. A project called HITIQA to develop high-quality interactive question answering. It describes the team members and key research issues like question semantics, human-computer dialogue, and information quality metrics.
Machine translation from English to HindiRajat Jain
Machine translation a part of natural language processing.The algorithm suggested is word based algorithm.We have done Translation from English to Hindi
submitted by
Garvita Sharma,10103467,B3
Rajat Jain,10103571,B6
IDENTIFYING THE SEMANTIC RELATIONS ON UNSTRUCTURED DATAijistjournal
Ontologisms have been applied to many applications in recent years, especially on Sematic Web, Information Retrieval, Information Extraction, and Question and Answer. The purpose of domain-specific ontology is to get rid of conceptual and terminological confusion. It accomplishes this by specifying a set of generic concepts that characterizes the domain as well as their definitions and interrelationships. This paper will describe some algorithms for identifying semantic relations and constructing an Information Technology Ontology, while extracting the concepts and objects from different sources. The Ontology is constructed based on three main resources: ACM, Wikipedia and unstructured files from ACM Digital Library. Our algorithms are combined of Natural Language Processing and Machine Learning. We use Natural Language Processing tools, such as OpenNLP, Stanford Lexical Dependency Parser in order to explore sentences. We then extract these sentences based on English pattern in order to build training set. We use a random sample among 245 categories of ACM to evaluate our results. Results generated show that our system yields superior performance.
From TREC to Watson: is open domain question answering a solved problem?Constantin Orasan
The document summarizes a presentation on question answering systems. It begins by providing context on information overload and defining question answering. It then discusses the evolution of QA systems from early databases to today's open-domain systems. The presentation focuses on IBM's Watson system, providing an overview of its unprecedented ability to answer open-domain questions as well as the massive resources required for its development. It concludes by arguing that open-domain QA remains unsolved and that closed-domain, interactive QA may be more practical for real-world applications.
A Comprehensive Review Of Automated Essay Scoring (AES) Research And DevelopmentAnna Landers
This document provides a comprehensive review of research on Automated Essay Scoring (AES). It discusses three common AES frameworks: Content Similarity, Machine Learning, and Hybrid. It summarizes key AES systems including Project Essay Grader, Intelligent Essay Assessor, IntelliMetric, and e-rater. It also reviews past literature on AES categorized by attribute (style, content, hybrid), methodology, prediction model, and findings. The review aims to analyze the development of AES and provide recommendations for its future.
Pedro is a tool that combines rapid data modelling and ontology services. It renders data entry forms from an XML schema, allowing users to create data files that conform to the data model. Users can mark up text fields with controlled vocabulary terms supplied by ontology services. Originally developed for a proteomics consortium, Pedro has since been used in other domains like genomics and security. It validates data files against the underlying data model and remembers where controlled vocabulary terms came from through its ontology services.
Query Translation for Data Sources with Heterogeneous Content Semantics Jie Bao
The document discusses query translation for data sources with heterogeneous content semantics. It proposes using ontology-extended data sources to make explicit the implicit ontologies associated with data. The key aspects covered include translating queries between different data content ontologies using conversion functions and interoperation constraints to ensure sound, complete, or exact translations.
Semantic Relatedness of Web Resources by XESA - Philipp SchollCROKODIl consortium
This document discusses using extended explicit semantic analysis (XESA) to measure semantic relatedness between short text snippets for recommendation purposes. It proposes enhancing ESA by incorporating additional semantic information from Wikipedia, such as article links and categories. An evaluation compares the performance of ESA, XESA using the article graph, XESA using categories, and a combination. The results show that XESA using the article graph improves over ESA by up to 9% and performs best for recommending related snippets.
French machine reading for question answeringAli Kabbadj
This paper proposes to unlock the main barrier to machine reading and comprehension French natural language texts. This open the way to machine to find to a question a precise answer buried in the mass of unstructured French texts. Or to create a universal French chatbot. Deep learning has produced extremely promising results for various tasks in natural language understanding particularly topic classification, sentiment analysis, question answering, and language translation. But to be effective Deep Learning methods need very large training da-tasets. Until now these technics cannot be actually used for French texts Question Answering (Q&A) applications since there was not a large Q&A training dataset. We produced a large (100 000+) French training Dataset for Q&A by translating and adapting the English SQuAD v1.1 Dataset, a GloVe French word and character embed-ding vectors from Wikipedia French Dump. We trained and evaluated of three different Q&A neural network ar-chitectures in French and carried out a French Q&A models with F1 score around 70%.
Semantic Interoperability - grafi della conoscenzaGiorgia Lodi
This document summarizes Giorgia Lodi's presentation on meaningful data and semantic interoperability in the Italian public sector. Lodi discusses issues with data quality such as missing values, semantics mismatches, and use of strings instead of codes. She argues that adopting semantic web standards like RDF, OWL and SPARQL can help address these issues by linking data together and representing it semantically. Ontologies and knowledge graphs can be used to represent domain knowledge and infer new facts. Tools like FRED can generate knowledge graphs from unstructured text. Overall, Lodi argues that semantic web technologies have the potential to improve data interoperability and quality in the public sector, though challenges remain.
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
Dissertation defense slides on "Semantic Analysis for Improved Multi-document...Quinsulon Israel
This document outlines Quinsulon Israel's Ph.D. dissertation defense on using semantic analysis to improve multi-document summarization. The dissertation examines using semantic triples clustering and semantic class scoring of sentences to generate summaries. It reviews prior work on statistical, features combination, graph-based, multi-level text relationship, and semantic analysis approaches. The dissertation aims to improve the baseline method and evaluate the effects of semantic analysis on focused multi-document summarization performance.
Ontology engineering of automatic text processing methodsIJECEIAES
Currently, ontologies are recognized as the most effective means of formalizing and systematizing knowledge and data in scientific subject area (SSA). Practice has shown that using ontology design patterns is effective in developing the ontology of scientific subject areas. This is due to the fact that scientific subject areas ontology, as a rule, contains a large number of typical fragments that are well described by patterns of ontology design. In the paper, we present an approach to ontology engineering of automatic text processing methods based on ontology design patterns. In order to get an ontology that would describe automatic text processing sufficiently fully, it is required to process a large number of scientific publications and information resources containing information from modeling area. It is possible to facilitate and speed up the process of updating ontology with information from such sources by using lexical and syntactic patterns of ontology design. Our ontology of automatic text processing will become the conceptual basis of an intelligent information resource on modern methods of automatic text processing, which will provide systematization of all information on these methods, its integration into a single information space, convenient navigation through it, as well as meaningful access to it.
The task of keyword extraction is to automatically identify a set of terms that best describe the document. Automatic keyword extraction establishes a foundation for various natural language processing applications: information retrieval, the automatic indexing and classification of documents, automatic summarization and high-level semantic description, etc. Although the keyword extraction applications usually work on single documents (document-oriented task), keyword extraction is also applicable to a more demanding task, i.e. the keyword extraction from a whole collection of documents or from an entire web site, or from tweets from Twitter. In the era of big-data, obtaining an effective and efficient method for automatic keyword extraction from huge amounts of multi-topic textual sources is of high importance.
We proposed a novel Selectivity-Based Keyword Extraction (SBKE) method, which extracts keywords from the source text represented as a network. The node selectivity value is calculated from a weighted network as the average weight distributed on the links of a single node and is used in the procedure of keyword candidate ranking and extraction. The selectivity slightly outperforms an extraction based on the standard centrality measures. Therefore, the selectivity and its modification – generalized selectivity as the node centrality measures are included in the SBKE method. Selectivity-based extraction does not require linguistic knowledge as it is derived purely from statistical and structural information of the network and it can be easily ported to new languages and used in a multilingual scenario. The true potential of the proposed SBKE method is in its generality, portability and low computation costs, which positions it as a strong candidate for preparing collections which lack human annotations for keyword extraction. Testing of the portability of the SBKE was tested on Croatian, Serbian and English texts – more precisely it was developed on Croatian News and ported for extraction from parallel abstracts of scientific publication in the Serbian and English languages.
The constructed parallel corpus of scientific abstracts with annotated keywords allows a better comparison of the performance of the method across languages since we have the controlled experimental environment and data. The achieved keyword extraction results measured with an F1 score are 49.57% for English and 46.73% for the Serbian language, if we disregard keywords that are not present in the abstracts. In case that we evaluate against the whole keyword set, the F1 scores are 40.08% and 45.71% respectively. This work shows that SBKE can be easily ported to new a language, domain and type of text in the sense of its structure. Still, there are drawbacks – the method can extract only the words that appear in the text.
The document discusses using inductive logic programming (ILP) to perform information extraction (IE) on biomedical texts. It outlines applying ILP to learn recursive theories from annotated examples to fill slots in templates and extract entities. The learning strategy involves searching for theories for each concept in parallel before discovering dependencies between concepts. Text processing involves tokenization, POS tagging and domain dictionaries before description generation for ILP.
The document discusses using inductive logic programming (ILP) to perform information extraction (IE) on biomedical texts. It outlines applying ILP to learn recursive theories from annotated examples to fill slots in templates and extract entities. The learning strategy involves searching for theories for each concept in parallel before discovering dependencies between concepts. Text processing involves annotating documents before using features of tokens for ILP learning.
IRJET- Short-Text Semantic Similarity using Glove Word EmbeddingIRJET Journal
The document describes a study that uses GloVe word embeddings to measure semantic similarity between short texts. GloVe is an unsupervised learning algorithm for obtaining vector representations of words. The study trains GloVe word embeddings on a large corpus, then uses the embeddings to encode short texts and calculate their semantic similarity, comparing the accuracy to methods that use Word2Vec embeddings. It aims to show that GloVe embeddings may provide better performance for short text semantic similarity tasks.
IRJET - Automated Essay Grading System using Deep LearningIRJET Journal
This document describes an automated essay grading system that uses deep learning techniques. It discusses how previous grading systems used machine learning algorithms like linear regression and support vector machines. It then presents a new system that uses an LSTM and dense neural network model to grade essays on a scale of 1-10. The system preprocesses essays by removing stopwords and numbers before converting the text to word vectors as input to the deep learning model. It aims to reduce the time spent on grading large numbers of essays compared to manual grading.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The document discusses using machine learning to identify factors that correlate with essay grades and English proficiency levels. It analyzes a database of 481 essays written by Dutch students and coded with 81 linguistic variables. Various machine learning algorithms are tested to predict the essays' proficiency levels as assigned by human raters. The best performing algorithm, Logistic Model Tree, achieves accuracy comparable to human raters when using all 81 features directly. Feature selection and discretization are also tested to identify a minimal set of highly predictive features.
The document discusses two NSF-funded research projects on intelligence and security informatics:
1. A project to filter and monitor message streams to detect "new events" and changes in topics or activity levels. It describes the technical challenges and components of automatic message processing.
2. A project called HITIQA to develop high-quality interactive question answering. It describes the team members and key research issues like question semantics, human-computer dialogue, and information quality metrics.
Similar to Possibility of applying fuzzy logic in the e-Learning system (20)
Temple of Asclepius in Thrace. Excavation resultsKrassimira Luka
The temple and the sanctuary around were dedicated to Asklepios Zmidrenus. This name has been known since 1875 when an inscription dedicated to him was discovered in Rome. The inscription is dated in 227 AD and was left by soldiers originating from the city of Philippopolis (modern Plovdiv).
Main Java[All of the Base Concepts}.docxadhitya5119
This is part 1 of my Java Learning Journey. This Contains Custom methods, classes, constructors, packages, multithreading , try- catch block, finally block and more.
Beyond Degrees - Empowering the Workforce in the Context of Skills-First.pptxEduSkills OECD
Iván Bornacelly, Policy Analyst at the OECD Centre for Skills, OECD, presents at the webinar 'Tackling job market gaps with a skills-first approach' on 12 June 2024
Walmart Business+ and Spark Good for Nonprofits.pdfTechSoup
"Learn about all the ways Walmart supports nonprofit organizations.
You will hear from Liz Willett, the Head of Nonprofits, and hear about what Walmart is doing to help nonprofits, including Walmart Business and Spark Good. Walmart Business+ is a new offer for nonprofits that offers discounts and also streamlines nonprofits order and expense tracking, saving time and money.
The webinar may also give some examples on how nonprofits can best leverage Walmart Business+.
The event will cover the following::
Walmart Business + (https://business.walmart.com/plus) is a new shopping experience for nonprofits, schools, and local business customers that connects an exclusive online shopping experience to stores. Benefits include free delivery and shipping, a 'Spend Analytics” feature, special discounts, deals and tax-exempt shopping.
Special TechSoup offer for a free 180 days membership, and up to $150 in discounts on eligible orders.
Spark Good (walmart.com/sparkgood) is a charitable platform that enables nonprofits to receive donations directly from customers and associates.
Answers about how you can do more with Walmart!"
हिंदी वर्णमाला पीपीटी, hindi alphabet PPT presentation, hindi varnamala PPT, Hindi Varnamala pdf, हिंदी स्वर, हिंदी व्यंजन, sikhiye hindi varnmala, dr. mulla adam ali, hindi language and literature, hindi alphabet with drawing, hindi alphabet pdf, hindi varnamala for childrens, hindi language, hindi varnamala practice for kids, https://www.drmullaadamali.com
it describes the bony anatomy including the femoral head , acetabulum, labrum . also discusses the capsule , ligaments . muscle that act on the hip joint and the range of motion are outlined. factors affecting hip joint stability and weight transmission through the joint are summarized.
LAND USE LAND COVER AND NDVI OF MIRZAPUR DISTRICT, UPRAHUL
This Dissertation explores the particular circumstances of Mirzapur, a region located in the
core of India. Mirzapur, with its varied terrains and abundant biodiversity, offers an optimal
environment for investigating the changes in vegetation cover dynamics. Our study utilizes
advanced technologies such as GIS (Geographic Information Systems) and Remote sensing to
analyze the transformations that have taken place over the course of a decade.
The complex relationship between human activities and the environment has been the focus
of extensive research and worry. As the global community grapples with swift urbanization,
population expansion, and economic progress, the effects on natural ecosystems are becoming
more evident. A crucial element of this impact is the alteration of vegetation cover, which plays a
significant role in maintaining the ecological equilibrium of our planet.Land serves as the foundation for all human activities and provides the necessary materials for
these activities. As the most crucial natural resource, its utilization by humans results in different
'Land uses,' which are determined by both human activities and the physical characteristics of the
land.
The utilization of land is impacted by human needs and environmental factors. In countries
like India, rapid population growth and the emphasis on extensive resource exploitation can lead
to significant land degradation, adversely affecting the region's land cover.
Therefore, human intervention has significantly influenced land use patterns over many
centuries, evolving its structure over time and space. In the present era, these changes have
accelerated due to factors such as agriculture and urbanization. Information regarding land use and
cover is essential for various planning and management tasks related to the Earth's surface,
providing crucial environmental data for scientific, resource management, policy purposes, and
diverse human activities.
Accurate understanding of land use and cover is imperative for the development planning
of any area. Consequently, a wide range of professionals, including earth system scientists, land
and water managers, and urban planners, are interested in obtaining data on land use and cover
changes, conversion trends, and other related patterns. The spatial dimensions of land use and
cover support policymakers and scientists in making well-informed decisions, as alterations in
these patterns indicate shifts in economic and social conditions. Monitoring such changes with the
help of Advanced technologies like Remote Sensing and Geographic Information Systems is
crucial for coordinated efforts across different administrative levels. Advanced technologies like
Remote Sensing and Geographic Information Systems
9
Changes in vegetation cover refer to variations in the distribution, composition, and overall
structure of plant communities across different temporal and spatial scales. These changes can
occur natural.
Pengantar Penggunaan Flutter - Dart programming language1.pptx
Possibility of applying fuzzy logic in the e-Learning system
1. Preaković, D., Remenar, V., Grgurević, I. Faculty of Traffic and Transport Sciences, University of Zagreb Vukelićeva 4, 10000 Zagreb, Croatia {dragan.perakovic, vladimir.remenar, ivan.grgurevic}@fpz.hr CEC IIS 2008 , Faculty of Organization and Informatics, Varaždin, 200 8 .