Written text is an important component in the process of knowledge acquisition and communication. Poorly written text fails to deliver clear ideas to the reader no matter how revolutionary and ground-breaking these ideas are. Providing text with good writing style is essential to transfer ideas smoothly. While we have sophisticated tools to check for stylistic problems in program code, we do not apply the same techniques for written text. In this paper we present TextLint, a rule-based tool to check for common style errors in natural language. TextLint provides a structural model of written text and an extensible rule-based checking mechanism.
Survey on Indian CLIR and MT systems in Marathi LanguageEditor IJCATR
Cross Language Information Retrieval (CLIR) deals with retrieving relevant information stored in a language different from
the language of user’s query. This helps users to express the information need in their native languages. Machine translation based (MTbased)
approach of CLIR uses existing machine translation techniques to provide automatic translation of queries. This paper covers the
research work done in CLIR and MT systems for Marathi language in India.
GENDER AND AUTHORSHIP CATEGORISATION OF ARABIC TEXT FROM TWITTER USING PPMijcsit
In this paper we present gender and authorship categorisation using the Prediction by Partial Matching(PPM) compression scheme for text from Twitter written in Arabic. The PPMD variant of the compression scheme with different orders was used to perform the categorisation. We also applied different machine learning algorithms such as Multinational Naïve Bayes (MNB), K-Nearest Neighbours (KNN), and an
implementation of Support Vector Machine (LIBSVM), applying the same processing steps for all the algorithms. PPMD shows significantly better accuracy in comparison to all the other machine learning algorithms, with order 11 PPMD working best, achieving 90 % and 96% accuracy for gender and
authorship respectively.
TEXT ADVERTISEMENTS ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKSijdms
In this paper, we describe the developed model of the Convolutional Neural Networks CNN to a
classification of advertisements. The developed method has been tested on both texts (Arabic and Slovak
texts).The advertisements are chosen on a classified advertisements websites as short texts. We evolved a
modified model of the CNN, we have implemented it and developed next modifications. We studied their
influence on the performing activity of the proposed network. The result is a functional model of the
network and its implementation in Java and Python. And analysis of model results using different
parameters for the network and input data. The results on experiments data show that the developed model
of CNN is useful in the domains of Arabic and Slovak short texts, mainly for some classification of
advertisements. This paper gives complete guidelines for authors submitting papers for the AIRCC
Journals.
This paper presents a natural language processing based automated system called DrawPlus for generating UML diagrams, user scenarios and test cases after analyzing the given business requirement specification which is written in natural language. The DrawPlus is presented for analyzing the natural languages and extracting the relative and required information from the given business requirement Specification by the user. Basically user writes the requirements specifications in simple English and the designed system has conspicuous ability to analyze the given requirement specification by using some of the core natural language processing techniques with our own well defined algorithms. After compound analysis and extraction of associated information, the DrawPlus system draws use case diagram, User scenarios and system level high level test case description. The DrawPlus provides the more convenient and reliable way of generating use case, user scenarios and test cases in a way reducing the time and cost of software development process while accelerating the 70 of works in Software design and Testing phase Janani Tharmaseelan ""Cohesive Software Design"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22900.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/22900/cohesive-software-design/janani-tharmaseelan
Compression-Based Parts-of-Speech Tagger for The Arabic LanguageCSCJournals
This paper explores the use of Compression-based models to train a Part-of-Speech (POS) tagger for the Arabic language. The newly developed tagger is based on the Prediction-by-Partial Matching (PPM) compression system, which has already been employed successfully in several NLP tasks. Several models were trained for the new tagger, the first models were trained using a silver-standard data from two different POS Arabic taggers, and the second model utilised the BAAC corpus, which is a 50K term manually annotated MSA corpus, where the PPM tagger achieved an accuracy of 93.07%. Also, the tag-based models were utilised to evaluate the performance of the new tagger by first tagging different Classical Arabic corpora and Modern Standard Arabic corpora then compressing the text using tag-based compression models. The results show that the use of silver-standard models has led to a reduction in the quality of the tag-based compression by an average of 0.43%, whereas the use of the gold-standard model has increased the tag-based compression quality by an average of 4.61% when used to tag Modern Standard Arabic text.
Cross lingual similarity discrimination with translation characteristicsijaia
In cross-lingual plagiarism detection, the similarity between sentences is the basis of judgment. This paper
proposes a discriminative model trained on bilingual corpus to divide a set of sentences in target language
into two classes according their similarities to a given sentence in source language. Positive outputs of the
discriminative model are then ranked according to the similarity probabilities. The translation candidates
of the given sentence are finally selected from the top-n positive results. One of the problems in model
building is the extremely imbalanced training data, in which positive samples are the translations of the
target sentences, while negative samples or the non-translations are numerous or unknown. We train models
on four kinds of sampling sets with same translation characteristics and compare their performances.
Experiments on the open dataset of 1500 pairs of English Chinese sentences are evaluated by three metrics
with satisfying performances, much higher than the baseline system.
Survey on Indian CLIR and MT systems in Marathi LanguageEditor IJCATR
Cross Language Information Retrieval (CLIR) deals with retrieving relevant information stored in a language different from
the language of user’s query. This helps users to express the information need in their native languages. Machine translation based (MTbased)
approach of CLIR uses existing machine translation techniques to provide automatic translation of queries. This paper covers the
research work done in CLIR and MT systems for Marathi language in India.
GENDER AND AUTHORSHIP CATEGORISATION OF ARABIC TEXT FROM TWITTER USING PPMijcsit
In this paper we present gender and authorship categorisation using the Prediction by Partial Matching(PPM) compression scheme for text from Twitter written in Arabic. The PPMD variant of the compression scheme with different orders was used to perform the categorisation. We also applied different machine learning algorithms such as Multinational Naïve Bayes (MNB), K-Nearest Neighbours (KNN), and an
implementation of Support Vector Machine (LIBSVM), applying the same processing steps for all the algorithms. PPMD shows significantly better accuracy in comparison to all the other machine learning algorithms, with order 11 PPMD working best, achieving 90 % and 96% accuracy for gender and
authorship respectively.
TEXT ADVERTISEMENTS ANALYSIS USING CONVOLUTIONAL NEURAL NETWORKSijdms
In this paper, we describe the developed model of the Convolutional Neural Networks CNN to a
classification of advertisements. The developed method has been tested on both texts (Arabic and Slovak
texts).The advertisements are chosen on a classified advertisements websites as short texts. We evolved a
modified model of the CNN, we have implemented it and developed next modifications. We studied their
influence on the performing activity of the proposed network. The result is a functional model of the
network and its implementation in Java and Python. And analysis of model results using different
parameters for the network and input data. The results on experiments data show that the developed model
of CNN is useful in the domains of Arabic and Slovak short texts, mainly for some classification of
advertisements. This paper gives complete guidelines for authors submitting papers for the AIRCC
Journals.
This paper presents a natural language processing based automated system called DrawPlus for generating UML diagrams, user scenarios and test cases after analyzing the given business requirement specification which is written in natural language. The DrawPlus is presented for analyzing the natural languages and extracting the relative and required information from the given business requirement Specification by the user. Basically user writes the requirements specifications in simple English and the designed system has conspicuous ability to analyze the given requirement specification by using some of the core natural language processing techniques with our own well defined algorithms. After compound analysis and extraction of associated information, the DrawPlus system draws use case diagram, User scenarios and system level high level test case description. The DrawPlus provides the more convenient and reliable way of generating use case, user scenarios and test cases in a way reducing the time and cost of software development process while accelerating the 70 of works in Software design and Testing phase Janani Tharmaseelan ""Cohesive Software Design"" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-3 , April 2019, URL: https://www.ijtsrd.com/papers/ijtsrd22900.pdf
Paper URL: https://www.ijtsrd.com/computer-science/other/22900/cohesive-software-design/janani-tharmaseelan
Compression-Based Parts-of-Speech Tagger for The Arabic LanguageCSCJournals
This paper explores the use of Compression-based models to train a Part-of-Speech (POS) tagger for the Arabic language. The newly developed tagger is based on the Prediction-by-Partial Matching (PPM) compression system, which has already been employed successfully in several NLP tasks. Several models were trained for the new tagger, the first models were trained using a silver-standard data from two different POS Arabic taggers, and the second model utilised the BAAC corpus, which is a 50K term manually annotated MSA corpus, where the PPM tagger achieved an accuracy of 93.07%. Also, the tag-based models were utilised to evaluate the performance of the new tagger by first tagging different Classical Arabic corpora and Modern Standard Arabic corpora then compressing the text using tag-based compression models. The results show that the use of silver-standard models has led to a reduction in the quality of the tag-based compression by an average of 0.43%, whereas the use of the gold-standard model has increased the tag-based compression quality by an average of 4.61% when used to tag Modern Standard Arabic text.
Cross lingual similarity discrimination with translation characteristicsijaia
In cross-lingual plagiarism detection, the similarity between sentences is the basis of judgment. This paper
proposes a discriminative model trained on bilingual corpus to divide a set of sentences in target language
into two classes according their similarities to a given sentence in source language. Positive outputs of the
discriminative model are then ranked according to the similarity probabilities. The translation candidates
of the given sentence are finally selected from the top-n positive results. One of the problems in model
building is the extremely imbalanced training data, in which positive samples are the translations of the
target sentences, while negative samples or the non-translations are numerous or unknown. We train models
on four kinds of sampling sets with same translation characteristics and compare their performances.
Experiments on the open dataset of 1500 pairs of English Chinese sentences are evaluated by three metrics
with satisfying performances, much higher than the baseline system.
STOCKGRAM : DEEP LEARNING MODEL FOR DIGITIZING FINANCIAL COMMUNICATIONS VIA N...kevig
This paper proposes a deep learning model, StockGram, to automate financial communications via natural language generation. StockGram is a seq2seq model that generates short and coherent versions of financial news reports based on the client's point of interest from numerous pools of verified resources. The proposed model is developed to mitigate the pain points of advisors who invest numerous hours while scanning through these news reports manually. StockGram leverages bi-directional LSTM cells that allows a recurrent system to make its prediction based on both past and future word sequences and hence predicts the next word in the sequence more precisely. The proposed model utilizes custom word-embeddings, GloVe, which incorporates global statistics to generate vector representations of news articles in an unsupervised manner and allows the model to converge faster. StockGram is evaluated based on the semantic closeness of the generated report to the provided prime words.
STOCKGRAM : DEEP LEARNING MODEL FOR DIGITIZING FINANCIAL COMMUNICATIONS VIA N...ijnlc
This paper proposes a deep learning model, StockGram, to automate financial communications via natural language generation. StockGram is a seq2seq model that generates short and coherent versions of financial news reports based on the client's point of interest from numerous pools of verified resources. The proposed model is developed to mitigate the pain points of advisors who invest numerous hours while
scanning through these news reports manually. StockGram leverages bi-directional LSTM cells that allows a recurrent system to make its prediction based on both past and future word sequences and hence predicts the next word in the sequence more precisely. The proposed model utilizes custom word-embeddings, GloVe, which incorporates global statistics to generate vector representations of news articles in an unsupervised manner and allows the model to converge faster. StockGram is evaluated based on the semantic closeness of the generated report to the provided prime words.
Sentence Validation by Statistical Language Modeling and Semantic RelationsEditor IJCATR
This paper deals with Sentence Validation - a sub-field of Natural Language Processing. It finds various applications in
different areas as it deals with understanding the natural language (English in most cases) and manipulating it. So the effort is on
understanding and extracting important information delivered to the computer and make possible efficient human computer
interaction. Sentence Validation is approached in two ways - by Statistical approach and Semantic approach. In both approaches
database is trained with the help of sample sentences of Brown corpus of NLTK. The statistical approach uses trigram technique based
on N-gram Markov Model and modified Kneser-Ney Smoothing to handle zero probabilities. As another testing on statistical basis,
tagging and chunking of the sentences having named entities is carried out using pre-defined grammar rules and semantic tree parsing,
and chunked off sentences are fed into another database, upon which testing is carried out. Finally, semantic analysis is carried out by
extracting entity relation pairs which are then tested. After the results of all three approaches is compiled, graphs are plotted and
variations are studied. Hence, a comparison of three different models is calculated and formulated. Graphs pertaining to the
probabilities of the three approaches are plotted, which clearly demarcate them and throw light on the findings of the project.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
GENERIC CODE CLONING METHOD FOR DETECTION OF CLONE CODE IN SOFTWARE DEVELOPMENT IAEME Publication
The major part of risk the development of software orprograms is existence ofduplicate code that can affect the software maintainability. The main aim of Clone
identification technique is to search and detect the parts of the software code which is
identical. In the passed there are various techniques that are used to identify andreflect the code identity and code fragments.Code cloning reduces the time and effort of the softwaredeveloper but it alsodecreases the quality of the software like readability, changeability and increasesmaintainability. So, code clone has to be detected to reducethe cost of maintenance tosome extent. In this paper, a new Generic technique is purposed to detect code clone
from various input source codes (from web, disk and etc.,) by segmenting the code intonumber of sub-programs or modules or functions. I propose a technique that candetect 1-type,2type, 3-type and 4-type clones efficiently.
This paper deals about the chunking of the Manipuri language, which is very highly agglutinative in
Nature. The system works in such a way that the Manipuri text is clean upto the gold standard. The text is
processed for Part of Speech (POS) tagging using Conditional Random Field (CRF). The output file is
treated as an input file for the CRF based Chunking system. The final output is a completely chunk tag
Manipuri text. The system shows a recall of 71.30%, a precision of 77.36% and a F-measure of 74.21%.
An Approach To Automatic Text Summarization Using Simplified Lesk Algorithm A...ijctcm
Text Summarization is a way to produce a text, which contains the significant portion of information of the
original text(s). Different methodologies are developed till now depending upon several parameters to find
the summary as the position, format and type of the sentences in an input text, formats of different words,
frequency of a particular word in a text etc. But according to different languages and input sources, these
parameters are varied. As result the performance of the algorithm is greatly affected. The proposed
approach summarizes a text without depending upon those parameters. Here, the relevance of the
sentences within the text is derived by Simplified Lesk algorithm and WordNet, an online dictionary. This
approach is not only independent of the format of the text and position of a sentence in a text, as the
sentences are arranged at first according to their relevance before the summarization process, the
percentage of summarization can be varied according to needs. The proposed approach gives around 80%
accurate results on 50% summarization of the original text with respect to the manually summarized result,
performed on 50 different types and lengths of texts. We have achieved satisfactory results even upto 25%
summarization of the original text.
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.
In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word
sense disambiguator systems that, given a word appearing in a certain context, can identify the sense of
that word. In this paper we consider the problem of deciding whether same words contained in different
documents are related to the same meaning or are homonyms. Our goal is to improve the estimate of the
similarity of documents in which some words may be used with different meanings. We present three new
strategies for solving this problem, which are used to filter out homonyms from the similarity computation.
Two of them are intrinsically non-semantic, whereas the other one has a semantic flavor and can also be
applied to word sense disambiguation. The three strategies have been embedded in an article document
recommendation system that one of the most important Italian ad-serving companies offers to its customers
Proposed Method for String Transformation using Probablistic ApproachEditor IJMTER
For this system the string is given as an input to the system generates the k most likely output strings corresponding to the input string. This system proposes both accurate and efficient feature by using a novel and probabilistic approach to string transformation, which is. The approach is includes the use of a log linear model, a method for training the model, and an algorithm for generating the top k candidates, whether there is or is not a predefined dictionary. The log linear model is defined as a conditional probability distribution of an output string and a rule set for the transformation conditioned on an input string. The learning method employs maximum likelihood estimation for parameter estimation. The string generation algorithm based on pruning is guaranteed to generate the optimal top k candidates. The proposed method will apply to correction of spelling errors in queries as well are formulation of queries in web search.
A Novel Approach for Rule Based Translation of English to Marathiaciijournal
This paper presents a design for rule-based machine translation system for English to Marathi language pair. The machine translation system will take input script as English sentence and parse with the help of Stanford parser. The Stanford parser will be used for main purposes on the source side processing, in the machine translation system. English to Marathi Bilingual dictionary is going to be created. The system will take the parsed output and separate the source text word by word and searches for their corresponding target words in the bilingual dictionary. The hand coded rules are written for Marathi inflections and also reordering rules are there. After applying the reordering rules, English sentence will be syntactically reordered to suit Marathi language
STOCKGRAM : DEEP LEARNING MODEL FOR DIGITIZING FINANCIAL COMMUNICATIONS VIA N...kevig
This paper proposes a deep learning model, StockGram, to automate financial communications via natural language generation. StockGram is a seq2seq model that generates short and coherent versions of financial news reports based on the client's point of interest from numerous pools of verified resources. The proposed model is developed to mitigate the pain points of advisors who invest numerous hours while scanning through these news reports manually. StockGram leverages bi-directional LSTM cells that allows a recurrent system to make its prediction based on both past and future word sequences and hence predicts the next word in the sequence more precisely. The proposed model utilizes custom word-embeddings, GloVe, which incorporates global statistics to generate vector representations of news articles in an unsupervised manner and allows the model to converge faster. StockGram is evaluated based on the semantic closeness of the generated report to the provided prime words.
STOCKGRAM : DEEP LEARNING MODEL FOR DIGITIZING FINANCIAL COMMUNICATIONS VIA N...ijnlc
This paper proposes a deep learning model, StockGram, to automate financial communications via natural language generation. StockGram is a seq2seq model that generates short and coherent versions of financial news reports based on the client's point of interest from numerous pools of verified resources. The proposed model is developed to mitigate the pain points of advisors who invest numerous hours while
scanning through these news reports manually. StockGram leverages bi-directional LSTM cells that allows a recurrent system to make its prediction based on both past and future word sequences and hence predicts the next word in the sequence more precisely. The proposed model utilizes custom word-embeddings, GloVe, which incorporates global statistics to generate vector representations of news articles in an unsupervised manner and allows the model to converge faster. StockGram is evaluated based on the semantic closeness of the generated report to the provided prime words.
Sentence Validation by Statistical Language Modeling and Semantic RelationsEditor IJCATR
This paper deals with Sentence Validation - a sub-field of Natural Language Processing. It finds various applications in
different areas as it deals with understanding the natural language (English in most cases) and manipulating it. So the effort is on
understanding and extracting important information delivered to the computer and make possible efficient human computer
interaction. Sentence Validation is approached in two ways - by Statistical approach and Semantic approach. In both approaches
database is trained with the help of sample sentences of Brown corpus of NLTK. The statistical approach uses trigram technique based
on N-gram Markov Model and modified Kneser-Ney Smoothing to handle zero probabilities. As another testing on statistical basis,
tagging and chunking of the sentences having named entities is carried out using pre-defined grammar rules and semantic tree parsing,
and chunked off sentences are fed into another database, upon which testing is carried out. Finally, semantic analysis is carried out by
extracting entity relation pairs which are then tested. After the results of all three approaches is compiled, graphs are plotted and
variations are studied. Hence, a comparison of three different models is calculated and formulated. Graphs pertaining to the
probabilities of the three approaches are plotted, which clearly demarcate them and throw light on the findings of the project.
IJRET : International Journal of Research in Engineering and Technology is an international peer reviewed, online journal published by eSAT Publishing House for the enhancement of research in various disciplines of Engineering and Technology. The aim and scope of the journal is to provide an academic medium and an important reference for the advancement and dissemination of research results that support high-level learning, teaching and research in the fields of Engineering and Technology. We bring together Scientists, Academician, Field Engineers, Scholars and Students of related fields of Engineering and Technology
GENERIC CODE CLONING METHOD FOR DETECTION OF CLONE CODE IN SOFTWARE DEVELOPMENT IAEME Publication
The major part of risk the development of software orprograms is existence ofduplicate code that can affect the software maintainability. The main aim of Clone
identification technique is to search and detect the parts of the software code which is
identical. In the passed there are various techniques that are used to identify andreflect the code identity and code fragments.Code cloning reduces the time and effort of the softwaredeveloper but it alsodecreases the quality of the software like readability, changeability and increasesmaintainability. So, code clone has to be detected to reducethe cost of maintenance tosome extent. In this paper, a new Generic technique is purposed to detect code clone
from various input source codes (from web, disk and etc.,) by segmenting the code intonumber of sub-programs or modules or functions. I propose a technique that candetect 1-type,2type, 3-type and 4-type clones efficiently.
This paper deals about the chunking of the Manipuri language, which is very highly agglutinative in
Nature. The system works in such a way that the Manipuri text is clean upto the gold standard. The text is
processed for Part of Speech (POS) tagging using Conditional Random Field (CRF). The output file is
treated as an input file for the CRF based Chunking system. The final output is a completely chunk tag
Manipuri text. The system shows a recall of 71.30%, a precision of 77.36% and a F-measure of 74.21%.
An Approach To Automatic Text Summarization Using Simplified Lesk Algorithm A...ijctcm
Text Summarization is a way to produce a text, which contains the significant portion of information of the
original text(s). Different methodologies are developed till now depending upon several parameters to find
the summary as the position, format and type of the sentences in an input text, formats of different words,
frequency of a particular word in a text etc. But according to different languages and input sources, these
parameters are varied. As result the performance of the algorithm is greatly affected. The proposed
approach summarizes a text without depending upon those parameters. Here, the relevance of the
sentences within the text is derived by Simplified Lesk algorithm and WordNet, an online dictionary. This
approach is not only independent of the format of the text and position of a sentence in a text, as the
sentences are arranged at first according to their relevance before the summarization process, the
percentage of summarization can be varied according to needs. The proposed approach gives around 80%
accurate results on 50% summarization of the original text with respect to the manually summarized result,
performed on 50 different types and lengths of texts. We have achieved satisfactory results even upto 25%
summarization of the original text.
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.
In recent years, great advances have been made in the speed, accuracy, and coverage of automatic word
sense disambiguator systems that, given a word appearing in a certain context, can identify the sense of
that word. In this paper we consider the problem of deciding whether same words contained in different
documents are related to the same meaning or are homonyms. Our goal is to improve the estimate of the
similarity of documents in which some words may be used with different meanings. We present three new
strategies for solving this problem, which are used to filter out homonyms from the similarity computation.
Two of them are intrinsically non-semantic, whereas the other one has a semantic flavor and can also be
applied to word sense disambiguation. The three strategies have been embedded in an article document
recommendation system that one of the most important Italian ad-serving companies offers to its customers
Proposed Method for String Transformation using Probablistic ApproachEditor IJMTER
For this system the string is given as an input to the system generates the k most likely output strings corresponding to the input string. This system proposes both accurate and efficient feature by using a novel and probabilistic approach to string transformation, which is. The approach is includes the use of a log linear model, a method for training the model, and an algorithm for generating the top k candidates, whether there is or is not a predefined dictionary. The log linear model is defined as a conditional probability distribution of an output string and a rule set for the transformation conditioned on an input string. The learning method employs maximum likelihood estimation for parameter estimation. The string generation algorithm based on pruning is guaranteed to generate the optimal top k candidates. The proposed method will apply to correction of spelling errors in queries as well are formulation of queries in web search.
A Novel Approach for Rule Based Translation of English to Marathiaciijournal
This paper presents a design for rule-based machine translation system for English to Marathi language pair. The machine translation system will take input script as English sentence and parse with the help of Stanford parser. The Stanford parser will be used for main purposes on the source side processing, in the machine translation system. English to Marathi Bilingual dictionary is going to be created. The system will take the parsed output and separate the source text word by word and searches for their corresponding target words in the bilingual dictionary. The hand coded rules are written for Marathi inflections and also reordering rules are there. After applying the reordering rules, English sentence will be syntactically reordered to suit Marathi language
Text detection and recognition from natural sceneshemanthmcqueen
Text characters in natural scenes and surroundings provide us with valuable information about the place and even provide us with some legal/important information. Hence it’s very important for us to detect such text and recognise them which helps a lot. But , it’s not really easy to recognize those text information because of the diverse backgrounds and fonts used for the text. In this paper, a method is proposed to extract the text information from the surroundings. First, a character descriptor is designed with existing standard detectors and descriptors. Then, character structure is modeled at each character class by designing stroke configuration maps.In natural scenes , the text part is generally found on nearby sign boards and other objects. The extraction of such text is difficult because of noisy backgrounds and diverse fonts and text sizes. But many applications have been proven to be efficient in extraction of text from surroundings. For this , the method of text extraction is divided into two processes;
Text detection
Text recognition
The presentation will describe an algorithm through which one can recognize Devanagari Characters. Devanagari is the script in which Hindi is represented. This algorithm
could automatically segment character from the image of Devenagari text and then recognize them.
For extracting the individual characters from the image of Devanagari text, algorithm segmented the image several
times using the vertical and horizontal projection.
The algorithm starts with first segmenting the lines separately from the document by taking horizontal projection and then the line
into words by taking vertical projection of the line. Another step which is particular to the separation of
Devanagari characters was required and was done by first removing the header line by finding horizontal projection
of each word. The characters can then be extracted by vertical projection of the word without the header line.
Algorithm uses a Kohonen Neural Netowrk for the recognition task. After the separation of the characters from the
image, the image matrix was then downsampled to bring it down to a fixed size so as to make the recognition
size independent. The matrix can then be fed as input neurons to the Kohonen Neural Network and the winning neuron is
found which identifies the recognized the character. This information in Kohonen Neural Network was stored
earlier during the training phase of the neural network. For this, we first assigned random weights from input neurons
to output neurons and then for each training set, the winning neuron was calculated by finding the maximum
output produced by the neurons. The wights for this winning neuron were then adjusted so that it responds to this
pattern more strongly the next time.
Robust Text Watermarking Technique for Authorship Protection of Hindi Languag...CSCJournals
Digital text documents have become a significantly important part on the Internet. A large number of users are attracted towards this digital form of text documents. But some security threats also arise concurrently. The digital libraries offer effective ways to access educational materials, government e-documents, financial documents, social media contents and many others. However content authorship and tamper detection of all these digital text documents require special attention. Till now, considerably very few digital watermarking techniques exist for text documents. In this paper, we propose a method for effective watermarking of Hindi language text documents. Hindi stands second among all languages across the world. It has widespread availability of its digital contents of various types. In proposed technique, the watermark is logically embedded in the text using 'swar' (vowel) as a special feature of the Hindi language, supported by suitable encryption. In extraction phase the Certificate Authority (CA) plays an important role in the authorship protection process as a trusted third party. The text is decrypted and watermark is extracted to prove genuine authorship. Our technique has been tested for various types of feasible text attacks with different embedding frequency.
PSEUDOCODE TO SOURCE PROGRAMMING LANGUAGE TRANSLATORijistjournal
Pseudocode is an artificial and informal language that helps developers to create algorithms. In this papera software tool is described, for translating the pseudocode into a particular source programminglanguage. This tool compiles the pseudocode given by the user and translates it to a source programminglanguage. The scope of the tool is very much wide as we can extend it to a universal programming toolwhich produces any of the specified programming language from a given pseudocode. Here we present thesolution for translating the pseudocode to a programming language by using the different stages of acompiler
FEATURES MATCHING USING NATURAL LANGUAGE PROCESSINGIJCI JOURNAL
The feature matching is a basic step in matching different datasets. This article proposes shows a new hybrid model of a pretrained Natural Language Processing (NLP) based model called BERT used in parallel with a statistical model based on Jaccard similarity to measure the similarity between list of features from two different datasets. This reduces the time required to search for correlations or manually match each feature from one dataset to another.
PYFML- A TEXTUAL LANGUAGE FOR FEATURE MODELINGijseajournal
The Feature model is a typical approach to capture variability in a software product line design and implementation. For that, most works automate feature model using a limited graphical notation represented by propositional logic and implemented by Prolog or Java programming languages. These works do not properly combine the extensions of classical feature models and do not provide scalability to implement large size problem issues. In this work, we propose a textual feature modeling language based on Python programming language (PyFML), that generalizes the classical feature models with instance feature cardinalities and attributes which be extended with highlight of replication and complex logical and mathematical cross-tree constraints. textX Meta-language is used for building PyFML to describe and organize feature model dependencies, and PyConstraint Problem Solver is used to implement feature model variability and its constraints validation. The work provides a textual human-readable language to represent feature model and maps the feature model descriptions directly into the object-oriented representation to be used by Constraint Problem Solver for computation. Furthermore, the proposed PyFML makes the notation of feature modeling more expressive to deal with complex software product line representations and using PyConstraint Problem Solver.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEijnlc
We propose an automatic classification system of movie genres based on different features from their textual synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis, and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
SOFTWARE TOOL FOR TRANSLATING PSEUDOCODE TO A PROGRAMMING LANGUAGEIJCI JOURNAL
Pseudocode is an artificial and informal language that helps programmers to develop algorithms. In this
paper a software tool is described, for translating the pseudocode into a particular programming
language. This tool takes the pseudocode as input, compiles it and translates it to a concrete programming
language. The scope of the tool is very much wide as we can extend it to a universal programming tool
which produces any of the specified programming language from a given pseudocode. Here we present the
solution for translating the pseudocode to a programming language by implementing the stages of a
compiler
SOFTWARE TOOL FOR TRANSLATING PSEUDOCODE TO A PROGRAMMING LANGUAGEIJCI JOURNAL
Pseudocode is an artificial and informal language that helps programmers to develop algorithms. In this
paper a software tool is described, for translating the pseudocode into a particular programming
language. This tool takes the pseudocode as input, compiles it and translates it to a concrete programming
language. The scope of the tool is very much wide as we can extend it to a universal programming tool
which produces any of the specified programming language from a given pseudocode. Here we present the
solution for translating the pseudocode to a programming language by implementing the stages of a
compiler.
A FILM SYNOPSIS GENRE CLASSIFIER BASED ON MAJORITY VOTEkevig
We propose an automatic classification system of movie genres based on different features from their textual
synopsis. Our system is first trained on thousands of movie synopsis from online open databases, by learning relationships between textual signatures and movie genres. Then it is tested on other movie synopsis,
and its results are compared to the true genres obtained from the Wikipedia and the Open Movie Database
(OMDB) databases. The results show that our algorithm achieves a classification accuracy exceeding 75%.
A Novel Approach for Rule Based Translation of English to Marathiaciijournal
This paper presents a design for rule-based machine translation system for English to Marathi language pair. The machine translation system will take input script as English sentence and parse with the help of Stanford parser. The Stanford parser will be used for main purposes on the source side processing, in the machine translation system. English to Marathi Bilingual dictionary is going to be created. The system will take the parsed output and separate the source text word by word and searches for their corresponding target words in the bilingual dictionary. The hand coded rules are written for Marathi inflections and also reordering rules are there. After applying the reordering rules, English sentence will be syntactically reordered to suit Marathi language
A Novel Approach for Rule Based Translation of English to Marathiaciijournal
This paper presents a design for rule-based machine translation system for English to Marathi language
pair. The machine translation system will take input script as English sentence and parse with the help of
Stanford parser. The Stanford parser will be used for main purposes on the source side processing, in the
machine translation system. English to Marathi Bilingual dictionary is going to be created. The system will
take the parsed output and separate the source text word by word and searches for their corresponding
target words in the bilingual dictionary. The hand coded rules are written for Marathi inflections and also
reordering rules are there. After applying the reordering rules, English sentence will be syntactically
reordered to suit Marathi language.
Automatic Labeling of the Object-oriented Source Code: The Lotus ApproachRa'Fat Al-Msie'deen
Most of open-source software systems become available on the internet today. Thus, we need automatic methods to label software code. Software code can be labeled with a set of keywords. These keywords in this paper referred as software labels. The goal of this paper is to provide a quick view of the software code vocabulary. This paper proposes an automatic approach to document the object-oriented software by labeling its code. The approach exploits all software identifiers to label software code. The paper presents the results of study conducted on the ArgoUML and drawing shapes case studies. Results showed that all code labels were correctly identified.
John Backus identified value-level (object-level) programming languages as programming languages
that combine various values to form other values until the final result values are obtained. Virtually
all our classic programming languages today including C, C++, and Java belong into this category.
Here we identify pattern-level (term-level) programming languages that combine various patterns
to form other patterns until the final result patterns are obtained. New patterns are constructed
from existing ones by the application of pattern-to-pattern functions exploiting pattern matching and
constructors. First-order logic programming languages such as Prolog, OBJ, and Maude belong into
this category. Our insight that pattern-level and value-level programming gives rise to a patternvalue duality is used as the foundation of the design of a new programming language called Asteroid.
Hallmarks of this new programming language design are the developer’s ability to explicitly control
the interpretation or model of expression terms and the notion of ‘patterns as first class citizens’.
In addition to a complete implementation of pattern-level programming Asteroid also supports an
object-oriented style of programming based on prototypes and also subject to pattern matching.
Similar to Natural Language Checking with Program Checking Tools (20)
PetitParser is a dynamic parser framework combining ideas from scannerless parsing, parser combinators, parsing expression grammars and packrat parsers. In this hands-on session we learn how to build simple parsers and how to model, test, compose and reuse complex grammars. Additionally, we will look at some tools and the reflective facilities provided by the PetitParser framework. Basic knowledge of the Smalltalk programming language is a requirement. Bring your laptop to reproduce the examples and solve some simple tasks.
Most today's software is highly static, even if it is written in a dynamic language like Smalltalk. Developers are not encouraged to extend the frameworks they are using; and end-users are unable to change the features of their software without initiating a new development effort. In contrast, extensible software is designed for change; and customizable software can be adapted to new needs without requiring an in-depth knowledge of the underlying implementation domain.
In this presentation I will investigate on how to write truly dynamic software and I will distill common patterns of software customizability. As running examples I present tools that I worked on during my path of discovering Smalltalk. One of these examples is Magritte, a dynamic meta-model that gives end-users the possibility to customize their applications without the need of an additional development effort. Another example is Helvetia, an infrastructure enabling on-the-fly customization of the programming language and development environment.
Dynamic Language Embedding With Homogeneous Tool SupportLukas Renggli
Domain-specific languages (DSLs) are increasingly used as embedded languages within general-purpose host languages. DSLs provide a compact, dedicated syntax for specifying parts of an application related to specialized domains. Unfortunately, such language extensions typically do not integrate well with existing development tools. Editors, compilers and debuggers are either unaware of the extensions, or must be adapted at a non-trivial cost. Furthermore, these embedded languages typically conflict with the grammar of the host language and make it difficult to write hybrid code; few mechanisms exist to control the scope and usage of multiple tightly interconnected embedded languages. In this dissertation we present Helvetia, a novel approach to embed languages into an existing host language by leveraging the underlying representation of the host language used by these tools. We introduce Language Boxes, an approach that offers a simple, modular mechanism to encapsulate (i) compositional changes to the host language, (ii) transformations to address various concerns such as compilation and syntax highlighting, and (iii) scoping rules to control visibility of fine-grained language changes. We describe the design and implementation of Helvetia and Language Boxes, discuss the required infrastructure of a host language enabling language embedding, and validate our approach by case studies that demonstrate different ways to extend or adapt the host language syntax and semantics.
Lint-like program checkers are popular tools that ensure code quality by verifying compliance with best practices for a particular programming language. The proliferation of internal domain-specific languages and models, however, poses new challenges for such tools. Traditional program checkers produce many false positives and fail to accurately check constraints, best practices, common errors, possible optimizations and portability issues particular to domain-specific languages. We advocate the use of dedicated rules to check domain-specific practices. We demonstrate the implementation of domain-specific rules, the automatic repair of violations, and their application to two case-studies: (1) Seaside defines several internal DSLs through a creative use of the syntax of the host language; and (2) Magritte adds meta-descriptions to existing code by means of special methods. Our empirical validation demonstrates that domain-specific program checking significantly improves code quality when compared with general purpose program checking.
Embedding Languages Without Breaking ToolsLukas Renggli
Domain-specific languages (DSLs) are increasingly used as embedded languages within general-purpose host languages. DSLs provide a compact, dedicated syntax for specifying parts of an application related to specialized domains. Unfortunately, such language extensions typically do not integrate well with the development tools of the host language. Editors, compilers and debuggers are either unaware of the extensions, or must be adapted at a non-trivial cost. We present a novel approach to embed DSLs into an existing host language by leveraging the underlying representation of the host language used by these tools. Helvetia is an extensible system that intercepts the compilation pipeline of the Smalltalk host language to seamlessly integrate language extensions. We validate our approach by case studies that demonstrate three fundamentally different ways to extend or adapt the host language syntax and semantics.
Language Boxes — Bending the Host Language with Modular Language ChangesLukas Renggli
As domain-specific modeling begins to attract widespread acceptance, pressure is increasing for the development of new domain-specific languages. Unfortunately these DSLs typically conflict with the grammar of the host language, making it difficult to compose hybrid code except at the level of strings; few mechanisms (if any) exist to control the scope of usage of multiple DSLs; and, most seriously, existing host language tools are typically unaware of the DSL extensions, thus hampering the development process. Language boxes address these issues by offering a simple, modular mechanism to encapsulate (i) compositional changes to the host language, (ii) transformations to address various concerns such as compilation and syntax highlighting, and (iii) scoping rules to control visibility of fine-grained language extensions.
We describe the design and implementation of language boxes, and show with the help of several examples how modular extensions can be introduced to a host language and environment.
Seaside is a web application framework that is written in Smalltalk. Smalltalk has been hugely influential on the development of computer languages but realistically how many people have ever used it? Seaside is a practical application of Smalltalk to the web sphere.
Magritte is a recursive meta-model to describe objects. The framework closely integrates into the reflective meta-model of Smalltalk. Providing an adaptive model enables not only developer, but also let end user build their own meta-models on the fly. Magritte allows one to easily instantiate views, editors, validators, parsers, object-factories, and mapping-tools on any meta-described object. The possibilites are endless. Describe once, get everywhere!
The Seaside web application framework is taken by storm. All major Smalltalk dialects have working ports of Seaside, contributing their particular strength to the mix. While Seaside itself tires to be dialect agnostic, vendors themselves are pushing in many different directions that are potentially incompatible. How does Seaside manage the compatibility among all these dialects? How does our dream Smalltalk vendor look like? How do we package the code in Seaside 2.9? And, most important, how does the future of Seaside look like?
Seaside does different to what is considered as best practices for Web development. It breaks with common best practices for Web development, such as to share as little state as possible, use clean and carefully chosen URLs, and use templates to separate model and presentation.
This talk will give a short introduction to a Web framework that is different by design. It will demonstrate what can be won when breaking the common patterns of Web development. Moreover it presents how Seaside integrates with the latest technologies such as AJAX and Comet.
Magritte - A Meta-Driven Approach to Empower Developers and End UsersLukas Renggli
Model-driven engineering is a powerful approach to build large-scale applications. However, an application's metamodel often remains static after the initial development phase and cannot be changed unless a new development effort occurs. Yet, end users often need to rapidly adapt their applications to new needs. In many cases, end users would know how to make the required adaptations, if only the application would let them do so. In this paper we present how we built a runtime-dynamic meta-environment into Smalltalk's reflective language model. Our solution offers the best of both worlds: developers can develop their applications using the same tools they are used to and gain the power of meta-programming. We show in particular that our approach is suitable to support end user customization without writing new code: the adaptive model of Magritte not only describes existing classes, but also lets end users build their own metamodels on the fly.
Concurrency control is mostly based on locks and is therefore notoriously difficult to use. Even though some programming languages provide high-level constructs, these add complexity and potentially hard-to-detect bugs to the application. Transactional memory is an attractive mechanism that does not have the drawbacks of locks, however the underlying implementation is often difficult to integrate into an existing language. In this paper we show how we have introduced transactional semantics into Smalltalk by using the reflective facilities of the language. Our approach is based on method annotations, incremental parse tree transformations and an optimistic commit protocol. The implementation does not depend on modifications to the virtual machine and therefor can be changed at the language level. We report on a practical case study, benchmarks and further and on-going work.
Seaside - Web Development As You Like ItLukas Renggli
Seaside does different to what is considered as best practices for Web development. It breaks with common best practices for Web development, such as to share as little state as possible, use clean and carefully chosen URLs, and use templates to separate model and presentation.
5 Steps to Mastering the Art of SeasideLukas Renggli
Seaside does things differently from what is considered best practice for Web development. Seaside breaks with common best practices, such as sharing as little state as possible, using clean and carefully chosen URLs, and using templates to separate model and presentation.
This tutorial will give a quick introduction to a Web framework that is different by design. It will demonstrate new patterns of Web development, that let you build highly interactive Web applications quickly, reusably and maintainably. Moreover it will show how Seaside integrates with latest technologies such as AJAX and Comet.
Lukas Renggli is a core developer of the Seaside web application framework. He has been using Seaside in industrial settings for more than 5 years. Lukas Renggli is the author of several frameworks built on top of Seaside, such as the Pier Content Management System.
Slack (or Teams) Automation for Bonterra Impact Management (fka Social Soluti...Jeffrey Haguewood
Sidekick Solutions uses Bonterra Impact Management (fka Social Solutions Apricot) and automation solutions to integrate data for business workflows.
We believe integration and automation are essential to user experience and the promise of efficient work through technology. Automation is the critical ingredient to realizing that full vision. We develop integration products and services for Bonterra Case Management software to support the deployment of automations for a variety of use cases.
This video focuses on the notifications, alerts, and approval requests using Slack for Bonterra Impact Management. The solutions covered in this webinar can also be deployed for Microsoft Teams.
Interested in deploying notification automations for Bonterra Impact Management? Contact us at sales@sidekicksolutionsllc.com to discuss next steps.
Essentials of Automations: Optimizing FME Workflows with ParametersSafe Software
Are you looking to streamline your workflows and boost your projects’ efficiency? Do you find yourself searching for ways to add flexibility and control over your FME workflows? If so, you’re in the right place.
Join us for an insightful dive into the world of FME parameters, a critical element in optimizing workflow efficiency. This webinar marks the beginning of our three-part “Essentials of Automation” series. This first webinar is designed to equip you with the knowledge and skills to utilize parameters effectively: enhancing the flexibility, maintainability, and user control of your FME projects.
Here’s what you’ll gain:
- Essentials of FME Parameters: Understand the pivotal role of parameters, including Reader/Writer, Transformer, User, and FME Flow categories. Discover how they are the key to unlocking automation and optimization within your workflows.
- Practical Applications in FME Form: Delve into key user parameter types including choice, connections, and file URLs. Allow users to control how a workflow runs, making your workflows more reusable. Learn to import values and deliver the best user experience for your workflows while enhancing accuracy.
- Optimization Strategies in FME Flow: Explore the creation and strategic deployment of parameters in FME Flow, including the use of deployment and geometry parameters, to maximize workflow efficiency.
- Pro Tips for Success: Gain insights on parameterizing connections and leveraging new features like Conditional Visibility for clarity and simplicity.
We’ll wrap up with a glimpse into future webinars, followed by a Q&A session to address your specific questions surrounding this topic.
Don’t miss this opportunity to elevate your FME expertise and drive your projects to new heights of efficiency.
Connector Corner: Automate dynamic content and events by pushing a buttonDianaGray10
Here is something new! In our next Connector Corner webinar, we will demonstrate how you can use a single workflow to:
Create a campaign using Mailchimp with merge tags/fields
Send an interactive Slack channel message (using buttons)
Have the message received by managers and peers along with a test email for review
But there’s more:
In a second workflow supporting the same use case, you’ll see:
Your campaign sent to target colleagues for approval
If the “Approve” button is clicked, a Jira/Zendesk ticket is created for the marketing design team
But—if the “Reject” button is pushed, colleagues will be alerted via Slack message
Join us to learn more about this new, human-in-the-loop capability, brought to you by Integration Service connectors.
And...
Speakers:
Akshay Agnihotri, Product Manager
Charlie Greenberg, Host
"Impact of front-end architecture on development cost", Viktor TurskyiFwdays
I have heard many times that architecture is not important for the front-end. Also, many times I have seen how developers implement features on the front-end just following the standard rules for a framework and think that this is enough to successfully launch the project, and then the project fails. How to prevent this and what approach to choose? I have launched dozens of complex projects and during the talk we will analyze which approaches have worked for me and which have not.
Kubernetes & AI - Beauty and the Beast !?! @KCD Istanbul 2024Tobias Schneck
As AI technology is pushing into IT I was wondering myself, as an “infrastructure container kubernetes guy”, how get this fancy AI technology get managed from an infrastructure operational view? Is it possible to apply our lovely cloud native principals as well? What benefit’s both technologies could bring to each other?
Let me take this questions and provide you a short journey through existing deployment models and use cases for AI software. On practical examples, we discuss what cloud/on-premise strategy we may need for applying it to our own infrastructure to get it to work from an enterprise perspective. I want to give an overview about infrastructure requirements and technologies, what could be beneficial or limiting your AI use cases in an enterprise environment. An interactive Demo will give you some insides, what approaches I got already working for real.
UiPath Test Automation using UiPath Test Suite series, part 3DianaGray10
Welcome to UiPath Test Automation using UiPath Test Suite series part 3. In this session, we will cover desktop automation along with UI automation.
Topics covered:
UI automation Introduction,
UI automation Sample
Desktop automation flow
Pradeep Chinnala, Senior Consultant Automation Developer @WonderBotz and UiPath MVP
Deepak Rai, Automation Practice Lead, Boundaryless Group and UiPath MVP
LF Energy Webinar: Electrical Grid Modelling and Simulation Through PowSyBl -...DanBrown980551
Do you want to learn how to model and simulate an electrical network from scratch in under an hour?
Then welcome to this PowSyBl workshop, hosted by Rte, the French Transmission System Operator (TSO)!
During the webinar, you will discover the PowSyBl ecosystem as well as handle and study an electrical network through an interactive Python notebook.
PowSyBl is an open source project hosted by LF Energy, which offers a comprehensive set of features for electrical grid modelling and simulation. Among other advanced features, PowSyBl provides:
- A fully editable and extendable library for grid component modelling;
- Visualization tools to display your network;
- Grid simulation tools, such as power flows, security analyses (with or without remedial actions) and sensitivity analyses;
The framework is mostly written in Java, with a Python binding so that Python developers can access PowSyBl functionalities as well.
What you will learn during the webinar:
- For beginners: discover PowSyBl's functionalities through a quick general presentation and the notebook, without needing any expert coding skills;
- For advanced developers: master the skills to efficiently apply PowSyBl functionalities to your real-world scenarios.
Smart TV Buyer Insights Survey 2024 by 91mobiles.pdf91mobiles
91mobiles recently conducted a Smart TV Buyer Insights Survey in which we asked over 3,000 respondents about the TV they own, aspects they look at on a new TV, and their TV buying preferences.
Neuro-symbolic is not enough, we need neuro-*semantic*Frank van Harmelen
Neuro-symbolic (NeSy) AI is on the rise. However, simply machine learning on just any symbolic structure is not sufficient to really harvest the gains of NeSy. These will only be gained when the symbolic structures have an actual semantics. I give an operational definition of semantics as “predictable inference”.
All of this illustrated with link prediction over knowledge graphs, but the argument is general.
Dev Dives: Train smarter, not harder – active learning and UiPath LLMs for do...UiPathCommunity
💥 Speed, accuracy, and scaling – discover the superpowers of GenAI in action with UiPath Document Understanding and Communications Mining™:
See how to accelerate model training and optimize model performance with active learning
Learn about the latest enhancements to out-of-the-box document processing – with little to no training required
Get an exclusive demo of the new family of UiPath LLMs – GenAI models specialized for processing different types of documents and messages
This is a hands-on session specifically designed for automation developers and AI enthusiasts seeking to enhance their knowledge in leveraging the latest intelligent document processing capabilities offered by UiPath.
Speakers:
👨🏫 Andras Palfi, Senior Product Manager, UiPath
👩🏫 Lenka Dulovicova, Product Program Manager, UiPath
15. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
16. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
.txt
.html
.tex
17. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
18. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
· The Markup models LATEX or HTML commands depending on the filetype
of the input.
All document elements answer the message text which returns a plain string
representation of the modeled text entity ignoring markup tokens. Furthermore
all elements know their source interval in the document. The relationship among
the elements in the model are depicted in Figure 3.
Element
text()
interval()
Document Paragraph Sentence Phrase
1 * 1 * 1 *
SyntacticElement
text()
interval()
Word Punctuation Whitespace Markup
1
*
1
*
Fig. 3. The TextLint model and the relationships between its classes.
3 From Strings to Objects
To build the high-level document model from the flat input string we use
PetitParser [7]. PetitParser is a framework targeted at parsing formal languages
(e.g., programming languages), but we employ it in this project to parse natural
4
19. raries: For parsing natural languages we use PetitParser [7], a flexible
rsing framework that makes it easy to define parsers and to dynamically
use, compose, transform and extend grammars. Furthermore, we use Glamour
, an engine for scripting browsers. Glamour reifies the notion of a browser
d defines the flow of data between different user interface widgets.
he contributions of this paper are:
1) we apply ideas from program checking to the domain of natural language;
2) we implement an object-oriented model used to represent natural text in
Smalltalk;
3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
representation of the modeled text entity ignoring markup tokens. Furthermore
all elements know their source interval in the document. The relationship among
the elements in the model are depicted in Figure 3.
Element
text()
interval()
Document Paragraph Sentence Phrase
1 * 1 * 1 *
SyntacticElement
text()
interval()
Word Punctuation Whitespace Markup
1
*
1
*
Fig. 3. The TextLint model and the relationships between its classes.
20. raries: For parsing natural languages we use PetitParser [7], a flexible
rsing framework that makes it easy to define parsers and to dynamically
use, compose, transform and extend grammars. Furthermore, we use Glamour
, an engine for scripting browsers. Glamour reifies the notion of a browser
d defines the flow of data between different user interface widgets.
he contributions of this paper are:
1) we apply ideas from program checking to the domain of natural language;
2) we implement an object-oriented model used to represent natural text in
Smalltalk;
3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
representation of the modeled text entity ignoring markup tokens. Furthermore
all elements know their source interval in the document. The relationship among
the elements in the model are depicted in Figure 3.
Element
text()
interval()
Document Paragraph Sentence Phrase
1 * 1 * 1 *
SyntacticElement
text()
interval()
Word Punctuation Whitespace Markup
1
*
1
*
Fig. 3. The TextLint model and the relationships between its classes.
21. raries: For parsing natural languages we use PetitParser [7], a flexible
rsing framework that makes it easy to define parsers and to dynamically
use, compose, transform and extend grammars. Furthermore, we use Glamour
, an engine for scripting browsers. Glamour reifies the notion of a browser
d defines the flow of data between different user interface widgets.
he contributions of this paper are:
1) we apply ideas from program checking to the domain of natural language;
2) we implement an object-oriented model used to represent natural text in
Smalltalk;
3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
representation of the modeled text entity ignoring markup tokens. Furthermore
all elements know their source interval in the document. The relationship among
the elements in the model are depicted in Figure 3.
Element
text()
interval()
Document Paragraph Sentence Phrase
1 * 1 * 1 *
SyntacticElement
text()
interval()
Word Punctuation Whitespace Markup
1
*
1
*
Fig. 3. The TextLint model and the relationships between its classes.
22. Other Language Models
raries: For parsing natural languages we use PetitParser [7], a flexible
rsing framework that makes it easy to define parsers and to dynamically
use, compose, transform and extend grammars. Furthermore, we use Glamour
, an engine for scripting browsers. Glamour reifies the notion of a browser
d defines the flow of data between different user interface widgets.
he contributions of this paper are:
1) we apply ideas from program checking to the domain of natural language;
2) we implement an object-oriented model used to represent natural text in
Smalltalk;
3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
representation of the modeled text entity ignoring markup tokens. Furthermore
all elements know their source interval in the document. The relationship among
the elements in the model are depicted in Figure 3.
Element
text()
interval()
Document Paragraph Sentence Phrase
1 * 1 * 1 *
SyntacticElement
text()
interval()
Word Punctuation Whitespace Markup
1
*
1
*
Fig. 3. The TextLint model and the relationships between its classes.
23. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
24. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
25. Avoid "a lot"
Avoid "a"
Avoid "allow to"
Avoid "an"
Avoid "as to whether"
Avoid "can not"
Avoid "case"
Avoid "certainly"
Avoid "could"
Avoid "currently"
Avoid "different than"
Avoid "doubt but"
Avoid "each and every one"
Avoid "enormity"
Avoid "factor"
Avoid "funny"
Avoid "help but"
Avoid "help to"
Avoid "however"
Avoid "importantly"
Avoid "in order to"
Avoid "in regards to"
Avoid "in terms of"
Avoid "insightful"
Avoid "interesting"
Avoid "irregardless"
Avoid "one of the most"
Avoid "regarded as"
Avoid "required to"
Avoid "somehow"
Avoid "stuff"
Avoid "the fact is"
Avoid "the fact that"
Avoid "the truth is"
Avoid "thing"
Avoid "thus"
Avoid "true fact"
Avoid "would"
Avoid comma
Avoid connectors repetition
Avoid continuous punctuation
Avoid continuous word repetition
Avoid contraction
Avoid joined sentences
Avoid long paragraph
Avoid long sentence
Avoid passive voice
Avoid qualifier
Avoid whitespace
Avoid word repetition
raries: For parsing natural languages we use PetitParser [7], a flexible
rsing framework that makes it easy to define parsers and to dynamically
use, compose, transform and extend grammars. Furthermore, we use Glamour
, an engine for scripting browsers. Glamour reifies the notion of a browser
d defines the flow of data between different user interface widgets.
he contributions of this paper are:
1) we apply ideas from program checking to the domain of natural language;
2) we implement an object-oriented model used to represent natural text in
Smalltalk;
3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
26. Avoid "a lot"
Avoid "a"
Avoid "allow to"
Avoid "an"
Avoid "as to whether"
Avoid "can not"
Avoid "case"
Avoid "certainly"
Avoid "could"
Avoid "currently"
Avoid "different than"
Avoid "doubt but"
Avoid "each and every one"
Avoid "enormity"
Avoid "factor"
Avoid "funny"
Avoid "help but"
Avoid "help to"
Avoid "however"
Avoid "importantly"
Avoid "in order to"
Avoid "in regards to"
Avoid "in terms of"
Avoid "insightful"
Avoid "interesting"
Avoid "irregardless"
Avoid "one of the most"
Avoid "regarded as"
Avoid "required to"
Avoid "somehow"
Avoid "stuff"
Avoid "the fact is"
Avoid "the fact that"
Avoid "the truth is"
Avoid "thing"
Avoid "thus"
Avoid "true fact"
Avoid "would"
Avoid comma
Avoid connectors repetition
Avoid continuous punctuation
Avoid continuous word repetition
Avoid contraction
Avoid joined sentences
Avoid long paragraph
Avoid long sentence
Avoid passive voice
Avoid qualifier
Avoid whitespace
Avoid word repetition
raries: For parsing natural languages we use PetitParser [7], a flexible
rsing framework that makes it easy to define parsers and to dynamically
use, compose, transform and extend grammars. Furthermore, we use Glamour
, an engine for scripting browsers. Glamour reifies the notion of a browser
d defines the flow of data between different user interface widgets.
he contributions of this paper are:
1) we apply ideas from program checking to the domain of natural language;
2) we implement an object-oriented model used to represent natural text in
Smalltalk;
3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
(self
word:
‘somehow’)
27. Avoid "a lot"
Avoid "a"
Avoid "allow to"
Avoid "an"
Avoid "as to whether"
Avoid "can not"
Avoid "case"
Avoid "certainly"
Avoid "could"
Avoid "currently"
Avoid "different than"
Avoid "doubt but"
Avoid "each and every one"
Avoid "enormity"
Avoid "factor"
Avoid "funny"
Avoid "help but"
Avoid "help to"
Avoid "however"
Avoid "importantly"
Avoid "in order to"
Avoid "in regards to"
Avoid "in terms of"
Avoid "insightful"
Avoid "interesting"
Avoid "irregardless"
Avoid "one of the most"
Avoid "regarded as"
Avoid "required to"
Avoid "somehow"
Avoid "stuff"
Avoid "the fact is"
Avoid "the fact that"
Avoid "the truth is"
Avoid "thing"
Avoid "thus"
Avoid "true fact"
Avoid "would"
Avoid comma
Avoid connectors repetition
Avoid continuous punctuation
Avoid continuous word repetition
Avoid contraction
Avoid joined sentences
Avoid long paragraph
Avoid long sentence
Avoid passive voice
Avoid qualifier
Avoid whitespace
Avoid word repetition
raries: For parsing natural languages we use PetitParser [7], a flexible
rsing framework that makes it easy to define parsers and to dynamically
use, compose, transform and extend grammars. Furthermore, we use Glamour
, an engine for scripting browsers. Glamour reifies the notion of a browser
d defines the flow of data between different user interface widgets.
he contributions of this paper are:
1) we apply ideas from program checking to the domain of natural language;
2) we implement an object-oriented model used to represent natural text in
Smalltalk;
3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
(self
punctuation)
,
(self
punctuation)
28. Avoid "a lot"
Avoid "a"
Avoid "allow to"
Avoid "an"
Avoid "as to whether"
Avoid "can not"
Avoid "case"
Avoid "certainly"
Avoid "could"
Avoid "currently"
Avoid "different than"
Avoid "doubt but"
Avoid "each and every one"
Avoid "enormity"
Avoid "factor"
Avoid "funny"
Avoid "help but"
Avoid "help to"
Avoid "however"
Avoid "importantly"
Avoid "in order to"
Avoid "in regards to"
Avoid "in terms of"
Avoid "insightful"
Avoid "interesting"
Avoid "irregardless"
Avoid "one of the most"
Avoid "regarded as"
Avoid "required to"
Avoid "somehow"
Avoid "stuff"
Avoid "the fact is"
Avoid "the fact that"
Avoid "the truth is"
Avoid "thing"
Avoid "thus"
Avoid "true fact"
Avoid "would"
Avoid comma
Avoid connectors repetition
Avoid continuous punctuation
Avoid continuous word repetition
Avoid contraction
Avoid joined sentences
Avoid long paragraph
Avoid long sentence
Avoid passive voice
Avoid qualifier
Avoid whitespace
Avoid word repetition
raries: For parsing natural languages we use PetitParser [7], a flexible
rsing framework that makes it easy to define parsers and to dynamically
use, compose, transform and extend grammars. Furthermore, we use Glamour
, an engine for scripting browsers. Glamour reifies the notion of a browser
d defines the flow of data between different user interface widgets.
he contributions of this paper are:
1) we apply ideas from program checking to the domain of natural language;
2) we implement an object-oriented model used to represent natural text in
Smalltalk;
3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
(self
wordIn:
#('am'
'are'
'were'
'being'
...
))
,
(self
separator
star)
,
((self
wordSatisfying:
[
:value
|
value
endsWith:
'ed'
])
/
(self
wordIn:
#('awoken'
'been'
'born'
'beat'
...
)))
29. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
30. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
scientificPaperStyle
:=
TLTextLintRule
allRules
-‐
TLWordRepetitionInParagraphRule
31. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
32. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
33. (2) we implement an object-oriented model used to represent natural text in
Smalltalk;
(3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
(4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
Fig. 2. Data Flow through TextLint.
Figure 2 gives an overview of the architecture of TextLint. Section 2 introduces
the natural text model of TextLint and Section 3 details how text documents
are parsed and the model is composed. Section 4 presents the rules which
model the stylistic checks. Section 5 describes how stylistic rules are defined in
34. raries: For parsing natural languages we use PetitParser [7], a flexible
rsing framework that makes it easy to define parsers and to dynamically
use, compose, transform and extend grammars. Furthermore, we use Glamour
, an engine for scripting browsers. Glamour reifies the notion of a browser
d defines the flow of data between different user interface widgets.
he contributions of this paper are:
1) we apply ideas from program checking to the domain of natural language;
2) we implement an object-oriented model used to represent natural text in
Smalltalk;
3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI
35. raries: For parsing natural languages we use PetitParser [7], a flexible
rsing framework that makes it easy to define parsers and to dynamically
use, compose, transform and extend grammars. Furthermore, we use Glamour
, an engine for scripting browsers. Glamour reifies the notion of a browser
d defines the flow of data between different user interface widgets.
he contributions of this paper are:
1) we apply ideas from program checking to the domain of natural language;
2) we implement an object-oriented model used to represent natural text in
Smalltalk;
3) we demonstrate a pattern matcher for the detection of style issues in
natural language; and
4) we demonstrate a graphical user interface that presents and explains the
problems detected by the tool.
Text Parsing Model Validation Failures
Rules Styles
GUI