The document describes a new probabilistic topic model called Learning To Summarize (LeToS) that aims to generate coherent multi-sentence summaries by modeling word and sentence transitions between grammatical and semantic roles (GSRs). LeToS represents documents as distributions over topics and GSR transitions, and generates words and sentences. It outperforms LDA on perplexity and generates summaries competitive with state-of-the-art on Pyramid evaluation. However, it has limitations in capturing factual information and understanding queries.
This document presents a method for measuring the semantic similarity of short texts using both corpus-based and knowledge-based measures of word semantic similarity. It combines word-to-word similarity scores with word specificity measures to determine the overall semantic similarity between two text segments. The method is evaluated on a paraphrase recognition task and is shown to outperform methods based only on simple lexical matching, resulting in up to a 13% reduction in error rate.
The document describes a system for semantic textual similarity (STS) that uses various techniques to estimate the semantic similarity between texts. The system combines lexical, syntactic, and semantic information sources using state-of-the-art algorithms. In SemEval 2016 tasks, the system achieved a mean Pearson correlation of 75.7% on the monolingual English task and 86.3% on the cross-lingual Spanish-English task, ranking first in the cross-lingual task. The system utilizes techniques such as word embeddings, paragraph vectors, tree-structured LSTMs, and word alignment to capture semantic similarity.
Chat bot using text similarity approachdinesh_joshy
1. There are three main techniques for chat bots to generate responses: static responses using templates, dynamic responses by scoring potential responses from a knowledge base, and generated responses using deep learning to generate novel responses from training data.
2. Text similarity can be measured using string-based, corpus-based, or knowledge-based approaches. String-based measures operate on character sequences while corpus-based measures use word co-occurrence statistics and knowledge-based measures use information from semantic networks like WordNet.
3. Popular corpus-based measures include LSA, ESA, and PMI-IR which analyze word contexts and co-occurrences in corpora. Knowledge-based measures like Resnik, Lin, and Leacock
This paper presents a new exemplar-based approach for word sense disambiguation (WSD) that integrates multiple knowledge sources. The authors' WSD system, called LEXAS, was tested on two datasets. On a common dataset involving the noun "interest", LEXAS achieved 87.4% accuracy, higher than previous work. LEXAS was also tested on a large dataset of 192,800 sense-tagged words, performing better than the most frequent sense heuristic on highly ambiguous words. This represents the largest test of a WSD system to date.
Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical
relations to address redundancy problem in text summarization. We first examined and redefined the type of rhetorical relations that is useful to retrieve sentences with identical content and performed the identification of those relations using SVMs. By exploiting the
rhetorical relations exist between sentences, we generate clusters of similar sentences from document sets. Then, cluster-based text summarization is performed using Conditional Markov Random Walk Model to measure the saliency scores of candidates summary. We evaluated our
method by measuring the cohesion and separation of the clusters and ROUGE score of generated summaries. The experimental result shows that our method performed well which shows promising potential of applying rhetorical relation in cluster-based text summarization.
The document proposes the Information Bottleneck Method as a way to extract relevant information from a signal X about another signal Y. It formalizes this as finding a compressed code for X that maximizes the information about Y while minimizing the code length. This forms a bottleneck that preserves only the most relevant information. The method provides self-consistent equations to determine the optimal coding rules from X to the code and from the code to Y. It generalizes rate-distortion theory by using the relationship between X and Y to determine relevance rather than requiring an externally specified distortion function.
The document presents a knowledge-based method for measuring semantic similarity between texts. It combines word-to-word semantic similarity metrics with information about word specificity to calculate a text-to-text similarity score. An example application shows how word similarity scores from WordNet are combined using the Wu & Palmer metric to determine the semantic similarity between two text segments. The method is evaluated on paraphrase identification tasks and shown to outperform approaches based only on lexical matching.
This document presents a method for measuring the semantic similarity of short texts using both corpus-based and knowledge-based measures of word semantic similarity. It combines word-to-word similarity scores with word specificity measures to determine the overall semantic similarity between two text segments. The method is evaluated on a paraphrase recognition task and is shown to outperform methods based only on simple lexical matching, resulting in up to a 13% reduction in error rate.
The document describes a system for semantic textual similarity (STS) that uses various techniques to estimate the semantic similarity between texts. The system combines lexical, syntactic, and semantic information sources using state-of-the-art algorithms. In SemEval 2016 tasks, the system achieved a mean Pearson correlation of 75.7% on the monolingual English task and 86.3% on the cross-lingual Spanish-English task, ranking first in the cross-lingual task. The system utilizes techniques such as word embeddings, paragraph vectors, tree-structured LSTMs, and word alignment to capture semantic similarity.
Chat bot using text similarity approachdinesh_joshy
1. There are three main techniques for chat bots to generate responses: static responses using templates, dynamic responses by scoring potential responses from a knowledge base, and generated responses using deep learning to generate novel responses from training data.
2. Text similarity can be measured using string-based, corpus-based, or knowledge-based approaches. String-based measures operate on character sequences while corpus-based measures use word co-occurrence statistics and knowledge-based measures use information from semantic networks like WordNet.
3. Popular corpus-based measures include LSA, ESA, and PMI-IR which analyze word contexts and co-occurrences in corpora. Knowledge-based measures like Resnik, Lin, and Leacock
This paper presents a new exemplar-based approach for word sense disambiguation (WSD) that integrates multiple knowledge sources. The authors' WSD system, called LEXAS, was tested on two datasets. On a common dataset involving the noun "interest", LEXAS achieved 87.4% accuracy, higher than previous work. LEXAS was also tested on a large dataset of 192,800 sense-tagged words, performing better than the most frequent sense heuristic on highly ambiguous words. This represents the largest test of a WSD system to date.
Many of previous research have proven that the usage of rhetorical relations is capable to enhance many applications such as text summarization, question answering and natural language generation. This work proposes an approach that expands the benefit of rhetorical
relations to address redundancy problem in text summarization. We first examined and redefined the type of rhetorical relations that is useful to retrieve sentences with identical content and performed the identification of those relations using SVMs. By exploiting the
rhetorical relations exist between sentences, we generate clusters of similar sentences from document sets. Then, cluster-based text summarization is performed using Conditional Markov Random Walk Model to measure the saliency scores of candidates summary. We evaluated our
method by measuring the cohesion and separation of the clusters and ROUGE score of generated summaries. The experimental result shows that our method performed well which shows promising potential of applying rhetorical relation in cluster-based text summarization.
The document proposes the Information Bottleneck Method as a way to extract relevant information from a signal X about another signal Y. It formalizes this as finding a compressed code for X that maximizes the information about Y while minimizing the code length. This forms a bottleneck that preserves only the most relevant information. The method provides self-consistent equations to determine the optimal coding rules from X to the code and from the code to Y. It generalizes rate-distortion theory by using the relationship between X and Y to determine relevance rather than requiring an externally specified distortion function.
The document presents a knowledge-based method for measuring semantic similarity between texts. It combines word-to-word semantic similarity metrics with information about word specificity to calculate a text-to-text similarity score. An example application shows how word similarity scores from WordNet are combined using the Wu & Palmer metric to determine the semantic similarity between two text segments. The method is evaluated on paraphrase identification tasks and shown to outperform approaches based only on lexical matching.
The document proposes a mixed approach using existing natural language processing techniques and novel techniques to automatically construct conceptual taxonomies from text. Key steps include identifying relevant concepts and attributes from text, clustering similar concepts, computing relevance weights for concepts, and generalizing concepts using WordNet. Preliminary results suggest the approach shows promise for extending and improving automatic taxonomy construction.
This document is a thesis submitted by Sihan Chen for a Master's degree in Statistics at the University of Chicago. It compares two topic models - Latent Dirichlet Allocation (LDA) and Von Mises-Fisher (vMF) clustering. LDA uses variational inference to approximate the posterior distribution of topics, while vMF clustering incorporates word embeddings. The thesis experiments with topic assignments, word co-occurrence, and pointwise mutual information to compare the two models.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Utterance Topic Model for Generating Coherent SummariesContent Savvy
This document proposes an utterance topic model (UTM) that incorporates grammatical and semantic role (GSR) transitions across sentences to model topics and local coherence in documents. The UTM extends latent Dirichlet allocation (LDA) by modeling topic distributions over GSR transitions rather than just word counts. The authors empirically show the UTM has lower perplexity than LDA on test data and evaluate its use in multi-document summarization using ROUGE and PYRAMID metrics on DUC2005, TAC2008 and TAC2009 datasets.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
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
Distributional semantics is a research area that uses statistical analysis of linguistic contexts to develop theories and methods for determining the semantic similarities between words and linguistic items based on their distributional properties in large text corpora. It is based on the distributional hypothesis that words with similar distributions have similar meanings. Distributional semantic models represent words as vectors in a high-dimensional semantic space based on their co-occurrence with other words, allowing semantic similarity to be measured using vector similarity methods. Common distributional semantic models include term frequency-inverse document frequency (tf-idf), latent semantic analysis (LSA), latent Dirichlet allocation (LDA), and word embeddings.
Introduction to Distributional SemanticsAndre Freitas
This document provides an introduction to distributional semantics. It discusses how distributional semantic models (DSMs) represent word meanings as vectors based on their linguistic contexts in large corpora. This distributional hypothesis states that words that appear in similar contexts tend to have similar meanings. The document outlines how DSMs are built, important parameters like context type and weighting, and examples like latent semantic analysis. It also discusses how DSMs can support applications like semantic search. Finally, it introduces how compositional semantics explores representing the meanings of phrases and sentences compositionally based on the meanings of their parts.
Tomoyuki Kajiwara, Kazuhide Yamamoto.
Noun Paraphrasing Based on a Variety of Contexts.
In Proceedings of the 28th Pacific Asia Conference on Language, Information and Computation (PACLIC 28), pp.644-649. Phuket, Thailand, December 2014.
The document introduces a new word analogy corpus for the Czech language to evaluate word embedding models. It contains over 22,000 semantic and syntactic analogy questions across various categories. The authors experiment with Word2Vec (CBOW and Skip-gram) and GloVe models on the Czech Wikipedia corpus. Their results show that CBOW generally performs best, with accuracy improving as vector dimension and training epochs increase. Performance is better on syntactic tasks than semantic ones. The new Czech analogy corpus allows further exploration of how word embeddings represent semantics and syntax for highly inflected languages.
Sergey Nikolenko and Elena Tutubalina - Constructing Aspect-Based Sentiment ...AIST
The document discusses techniques for constructing aspect-based sentiment lexicons using topic modeling. It presents an overview of sentiment analysis and existing topic modeling approaches for sentiment. The paper proposes a method to extend existing sentiment dictionaries by learning word sentiment priors automatically through an expectation-maximization algorithm applied to sentiment-topic models. Experimental results on a Russian reviews dataset show the approach improves sentiment classification compared to using a manually constructed lexicon alone.
Understanding Natural Languange with Corpora-based Generation of Dependency G...Edmond Lepedus
This document discusses training a dependency parser using an unparsed corpus rather than a manually parsed training set. It develops an iterative training method that generates training examples using heuristics from past parsing decisions. The method is shown to produce parse trees qualitatively similar to conventionally trained parsers. Three avenues for future research using this corpus-based generation method are proposed.
Corpus-based part-of-speech disambiguation of PersianIDES Editor
In this paper we introduce a method for part-ofspeech
disambiguation of Persian texts, which uses word class
probabilities in a relatively small training corpus in order to
automatically tag unrestricted Persian texts. The experiment
has been carried out in two levels as unigram and bi-gram
genotypes disambiguation. Comparing the results gained from
the two levels, we show that using immediate right context to
which a given word belongs can increase the accuracy rate of
the system to a high degree
An introduction to compositional models in distributional semanticsAndre Freitas
The document provides an overview of compositional distributional semantic models, which aim to develop principled and effective semantic models for real-world language use. It discusses using large corpora to extract distributional representations of word meanings and developing compositional models that combine these representations according to syntactic structure. Both additive and multiplicative mixture models as well as function-based models are described. Challenges including lack of training data and computational complexity are also outlined.
AN INVESTIGATION OF THE SAMPLING-BASED ALIGNMENT METHOD AND ITS CONTRIBUTIONSijaia
This document summarizes an investigation into improving the performance of a sampling-based alignment method for statistical machine translation. It proposes two contributions: 1) A method to enforce alignment of n-grams in distinct translation subtables to increase the number of longer n-grams, and 2) Examining combining phrase translation tables from the sampling method and MGIZA++, finding it slightly outperforms MGIZA++ alone and helps reduce out-of-vocabulary words. The method divides the parallel corpus into "unigramized" source-target n-gram subtables, runs the sampling aligner on each, and merges the subtables' phrase tables.
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box RuleML
The traditional semantics for First Order Logic (sometimes called Tarskian semantics) is based on the notion of interpretations of constants. Herbrand semantics is an alternative semantics based directly on truth assignments for ground sentences rather than interpretations of constants. Herbrand semantics is simpler and more intuitive than Tarskian semantics; and, consequently, it is easier to teach and learn. Moreover, it is more expressive. For example, while it is not possible to finitely axiomatize integer arithmetic with Tarskian semantics, this can be done easily with Herbrand Semantics. The downside is a loss of some common logical properties, such as compactness and completeness. However, there is no loss of inferential power. Anything that can be proved according to Tarskian semantics can also be proved according to Herbrand semantics. In this presentation, we define Herbrand semantics; we look at the implications for research on logic and rules systems and automated reasoning; and and we assess the potential for popularizing logic.
This document discusses text summarization using machine learning. It begins by defining text summarization as reducing a text to create a summary that retains the most important points. There are two main types: single document summarization and multiple document summarization. Extractive summarization creates summaries by extracting phrases or sentences from the source text, while abstractive summarization expresses ideas using different words. Supervised machine learning approaches use labeled training data to train classifiers to select content, while unsupervised approaches select content based on metrics like term frequency-inverse document frequency. ROUGE is commonly used to automatically evaluate summaries by comparing them to human references. Query-focused multi-document summarization aims to answer a user's information need by summarizing relevant documents
Word sense disambiguation using wsd specific wordnet of polysemy wordsijnlc
This paper presents a new model of WordNet that is used to disambiguate the correct sense of polysemy
word based on the clue words. The related words for each sense of a polysemy word as well as single sense
word are referred to as the clue words. The conventional WordNet organizes nouns, verbs, adjectives and
adverbs together into sets of synonyms called synsets each expressing a different concept. In contrast to the
structure of WordNet, we developed a new model of WordNet that organizes the different senses of
polysemy words as well as the single sense words based on the clue words. These clue words for each sense
of a polysemy word as well as for single sense word are used to disambiguate the correct meaning of the
polysemy word in the given context using knowledge based Word Sense Disambiguation (WSD) algorithms.
The clue word can be a noun, verb, adjective or adverb.
The document summarizes a tutorial on measuring semantic similarity and relatedness between medical concepts. It introduces different types of measures, including path-based measures, measures using information content that incorporate concept specificity, and measures of relatedness that use definition overlaps or corpus co-occurrence information. The tutorial aims to explain the distinction between similarity and relatedness, describe available measures, and how to evaluate and apply them in clinical natural language processing tasks.
This document discusses methods for aligning word senses between languages using probabilistic sense distributions. It proposes two approaches: 1) Using only monolingual corpora and aligning senses based on similar sense distributions between closely related languages. 2) Leveraging parallel corpora to estimate sense distribution alignments and the most probable translation for each source sense. The approaches are tested on the Europarl corpus, first ignoring and then exploiting sentence alignments. Several examples are examined to validate the sense alignments. Key aspects include using word sense disambiguation to annotate corpora, estimating sense assignment distributions, and assigning translation weights between language pairs based on relative sense frequencies.
Perseus' utterances that most inspired the group were:
1) "I would willingly risk my life to do so" which showed his courage in being willing to do something frightening.
2) "And, besides, what would my dear mother do, if her beloved son were turned into a stone?" which displayed his strength and desire to protect his mother.
3) "If anybody is in fault, it is myself; for I have the honor to hold your very brilliant and excellent eye in my own hand!" which demonstrated his determination to complete the challenges despite the risks.
Visualizing Text: Seth Redmore at the 2015 Smart Data Conferencesredmore
Seth Redmore talks about text and data visualization at this year's Smart Data Conference.
He covers:
-Common software packages for visualization
-Structured plots for unstructured text: Lines vs. bars vs. boxplots vs. piecharts vs. bubble charts
-Less structured plots: word clouds vs. treemaps vs. clusters vs. graphs
-Moving plots: animations over time
The document proposes a mixed approach using existing natural language processing techniques and novel techniques to automatically construct conceptual taxonomies from text. Key steps include identifying relevant concepts and attributes from text, clustering similar concepts, computing relevance weights for concepts, and generalizing concepts using WordNet. Preliminary results suggest the approach shows promise for extending and improving automatic taxonomy construction.
This document is a thesis submitted by Sihan Chen for a Master's degree in Statistics at the University of Chicago. It compares two topic models - Latent Dirichlet Allocation (LDA) and Von Mises-Fisher (vMF) clustering. LDA uses variational inference to approximate the posterior distribution of topics, while vMF clustering incorporates word embeddings. The thesis experiments with topic assignments, word co-occurrence, and pointwise mutual information to compare the two models.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Utterance Topic Model for Generating Coherent SummariesContent Savvy
This document proposes an utterance topic model (UTM) that incorporates grammatical and semantic role (GSR) transitions across sentences to model topics and local coherence in documents. The UTM extends latent Dirichlet allocation (LDA) by modeling topic distributions over GSR transitions rather than just word counts. The authors empirically show the UTM has lower perplexity than LDA on test data and evaluate its use in multi-document summarization using ROUGE and PYRAMID metrics on DUC2005, TAC2008 and TAC2009 datasets.
The spread and abundance of electronic documents requires automatic techniques for extracting useful information from the text they contain. The availability of conceptual taxonomies can be of great help, but manually building them is a complex and costly task. Building on previous work, we propose a technique to automatically extract conceptual graphs from text and reason with them. Since automated learning of taxonomies needs to be robust with respect to missing or partial knowledge and flexible with respect to noise, this work proposes a way to deal with these problems. The case of poor data/sparse concepts is tackled by finding generalizations among disjoint pieces of knowledge. Noise is
handled by introducing soft relationships among concepts rather than hard ones, and applying a probabilistic inferential setting. In particular, we propose to reason on the extracted graph using different kinds of relationships among concepts, where each arc/relationship is associated to a number that represents its likelihood among all possible worlds, and to face the problem of sparse knowledge by using generalizations among distant concepts as bridges between disjoint portions of knowledge.
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
Distributional semantics is a research area that uses statistical analysis of linguistic contexts to develop theories and methods for determining the semantic similarities between words and linguistic items based on their distributional properties in large text corpora. It is based on the distributional hypothesis that words with similar distributions have similar meanings. Distributional semantic models represent words as vectors in a high-dimensional semantic space based on their co-occurrence with other words, allowing semantic similarity to be measured using vector similarity methods. Common distributional semantic models include term frequency-inverse document frequency (tf-idf), latent semantic analysis (LSA), latent Dirichlet allocation (LDA), and word embeddings.
Introduction to Distributional SemanticsAndre Freitas
This document provides an introduction to distributional semantics. It discusses how distributional semantic models (DSMs) represent word meanings as vectors based on their linguistic contexts in large corpora. This distributional hypothesis states that words that appear in similar contexts tend to have similar meanings. The document outlines how DSMs are built, important parameters like context type and weighting, and examples like latent semantic analysis. It also discusses how DSMs can support applications like semantic search. Finally, it introduces how compositional semantics explores representing the meanings of phrases and sentences compositionally based on the meanings of their parts.
Tomoyuki Kajiwara, Kazuhide Yamamoto.
Noun Paraphrasing Based on a Variety of Contexts.
In Proceedings of the 28th Pacific Asia Conference on Language, Information and Computation (PACLIC 28), pp.644-649. Phuket, Thailand, December 2014.
The document introduces a new word analogy corpus for the Czech language to evaluate word embedding models. It contains over 22,000 semantic and syntactic analogy questions across various categories. The authors experiment with Word2Vec (CBOW and Skip-gram) and GloVe models on the Czech Wikipedia corpus. Their results show that CBOW generally performs best, with accuracy improving as vector dimension and training epochs increase. Performance is better on syntactic tasks than semantic ones. The new Czech analogy corpus allows further exploration of how word embeddings represent semantics and syntax for highly inflected languages.
Sergey Nikolenko and Elena Tutubalina - Constructing Aspect-Based Sentiment ...AIST
The document discusses techniques for constructing aspect-based sentiment lexicons using topic modeling. It presents an overview of sentiment analysis and existing topic modeling approaches for sentiment. The paper proposes a method to extend existing sentiment dictionaries by learning word sentiment priors automatically through an expectation-maximization algorithm applied to sentiment-topic models. Experimental results on a Russian reviews dataset show the approach improves sentiment classification compared to using a manually constructed lexicon alone.
Understanding Natural Languange with Corpora-based Generation of Dependency G...Edmond Lepedus
This document discusses training a dependency parser using an unparsed corpus rather than a manually parsed training set. It develops an iterative training method that generates training examples using heuristics from past parsing decisions. The method is shown to produce parse trees qualitatively similar to conventionally trained parsers. Three avenues for future research using this corpus-based generation method are proposed.
Corpus-based part-of-speech disambiguation of PersianIDES Editor
In this paper we introduce a method for part-ofspeech
disambiguation of Persian texts, which uses word class
probabilities in a relatively small training corpus in order to
automatically tag unrestricted Persian texts. The experiment
has been carried out in two levels as unigram and bi-gram
genotypes disambiguation. Comparing the results gained from
the two levels, we show that using immediate right context to
which a given word belongs can increase the accuracy rate of
the system to a high degree
An introduction to compositional models in distributional semanticsAndre Freitas
The document provides an overview of compositional distributional semantic models, which aim to develop principled and effective semantic models for real-world language use. It discusses using large corpora to extract distributional representations of word meanings and developing compositional models that combine these representations according to syntactic structure. Both additive and multiplicative mixture models as well as function-based models are described. Challenges including lack of training data and computational complexity are also outlined.
AN INVESTIGATION OF THE SAMPLING-BASED ALIGNMENT METHOD AND ITS CONTRIBUTIONSijaia
This document summarizes an investigation into improving the performance of a sampling-based alignment method for statistical machine translation. It proposes two contributions: 1) A method to enforce alignment of n-grams in distinct translation subtables to increase the number of longer n-grams, and 2) Examining combining phrase translation tables from the sampling method and MGIZA++, finding it slightly outperforms MGIZA++ alone and helps reduce out-of-vocabulary words. The method divides the parallel corpus into "unigramized" source-target n-gram subtables, runs the sampling aligner on each, and merges the subtables' phrase tables.
RuleML2015 The Herbrand Manifesto - Thinking Inside the Box RuleML
The traditional semantics for First Order Logic (sometimes called Tarskian semantics) is based on the notion of interpretations of constants. Herbrand semantics is an alternative semantics based directly on truth assignments for ground sentences rather than interpretations of constants. Herbrand semantics is simpler and more intuitive than Tarskian semantics; and, consequently, it is easier to teach and learn. Moreover, it is more expressive. For example, while it is not possible to finitely axiomatize integer arithmetic with Tarskian semantics, this can be done easily with Herbrand Semantics. The downside is a loss of some common logical properties, such as compactness and completeness. However, there is no loss of inferential power. Anything that can be proved according to Tarskian semantics can also be proved according to Herbrand semantics. In this presentation, we define Herbrand semantics; we look at the implications for research on logic and rules systems and automated reasoning; and and we assess the potential for popularizing logic.
This document discusses text summarization using machine learning. It begins by defining text summarization as reducing a text to create a summary that retains the most important points. There are two main types: single document summarization and multiple document summarization. Extractive summarization creates summaries by extracting phrases or sentences from the source text, while abstractive summarization expresses ideas using different words. Supervised machine learning approaches use labeled training data to train classifiers to select content, while unsupervised approaches select content based on metrics like term frequency-inverse document frequency. ROUGE is commonly used to automatically evaluate summaries by comparing them to human references. Query-focused multi-document summarization aims to answer a user's information need by summarizing relevant documents
Word sense disambiguation using wsd specific wordnet of polysemy wordsijnlc
This paper presents a new model of WordNet that is used to disambiguate the correct sense of polysemy
word based on the clue words. The related words for each sense of a polysemy word as well as single sense
word are referred to as the clue words. The conventional WordNet organizes nouns, verbs, adjectives and
adverbs together into sets of synonyms called synsets each expressing a different concept. In contrast to the
structure of WordNet, we developed a new model of WordNet that organizes the different senses of
polysemy words as well as the single sense words based on the clue words. These clue words for each sense
of a polysemy word as well as for single sense word are used to disambiguate the correct meaning of the
polysemy word in the given context using knowledge based Word Sense Disambiguation (WSD) algorithms.
The clue word can be a noun, verb, adjective or adverb.
The document summarizes a tutorial on measuring semantic similarity and relatedness between medical concepts. It introduces different types of measures, including path-based measures, measures using information content that incorporate concept specificity, and measures of relatedness that use definition overlaps or corpus co-occurrence information. The tutorial aims to explain the distinction between similarity and relatedness, describe available measures, and how to evaluate and apply them in clinical natural language processing tasks.
This document discusses methods for aligning word senses between languages using probabilistic sense distributions. It proposes two approaches: 1) Using only monolingual corpora and aligning senses based on similar sense distributions between closely related languages. 2) Leveraging parallel corpora to estimate sense distribution alignments and the most probable translation for each source sense. The approaches are tested on the Europarl corpus, first ignoring and then exploiting sentence alignments. Several examples are examined to validate the sense alignments. Key aspects include using word sense disambiguation to annotate corpora, estimating sense assignment distributions, and assigning translation weights between language pairs based on relative sense frequencies.
Perseus' utterances that most inspired the group were:
1) "I would willingly risk my life to do so" which showed his courage in being willing to do something frightening.
2) "And, besides, what would my dear mother do, if her beloved son were turned into a stone?" which displayed his strength and desire to protect his mother.
3) "If anybody is in fault, it is myself; for I have the honor to hold your very brilliant and excellent eye in my own hand!" which demonstrated his determination to complete the challenges despite the risks.
Visualizing Text: Seth Redmore at the 2015 Smart Data Conferencesredmore
Seth Redmore talks about text and data visualization at this year's Smart Data Conference.
He covers:
-Common software packages for visualization
-Structured plots for unstructured text: Lines vs. bars vs. boxplots vs. piecharts vs. bubble charts
-Less structured plots: word clouds vs. treemaps vs. clusters vs. graphs
-Moving plots: animations over time
Slides from my presentation at JCDL 2007.
The paper was titled "World Explorer: Visualizing Aggregate Data from Unstructured Text in Geo-Referenced Collections" and won the Vannevar Bush Best Paper award. You can read the full paper at http://www.rahulnair.net/files/JCDL07-ahern-WorldExplorer.pdf and also see a demo at http://tagmaps.research.yahoo.com/worldexplorer.php
The document discusses text summarization using the TextRank algorithm. TextRank takes relevant sentences from a body of text to create a summary. It involves tokenizing sentences or words to create a "bag of words", converting this to a graph, then using PageRank to rank sentences. This ranks the most important sentences to create a summary in a few steps. The document provides an overview of the TextRank summarization process and resources for learning more.
Text as Shape, Text as Meaning: Papyrology and Dotremont’s « Logogrammes »
Talk at the "Making Traces Symposium"
Univ of Southern Denmark, Odense
19th November 2014
This document discusses visualizing text and the tools and techniques used for text visualization. It begins by explaining that visualizing text can reveal patterns, clusters, trends, gaps and outliers in the data. It then discusses Anscombe's quartet to show how the form of presentation can reveal patterns. The document covers different types of text visualization including term counting, word clouds, terms in context, document comparison, document networks, and topic visualization. It also discusses tools used for the visualizations like MongoDB, Google Refine, Apache Solr, D3.js, and Mallet topic modeling.
The document provides various analogies to illustrate the structure of a well-written paragraph, including a hamburger, pyramid, cloud, and artichoke. It emphasizes that all sentences should clearly relate back to and support the main topic sentence. A unified paragraph contains a single focus, with every sentence explaining, exemplifying or expanding on the central idea without irrelevant facts.
The Role of Natural Language Processing in Information RetrievalTony Russell-Rose
The document discusses the role of natural language processing (NLP) in information retrieval. It provides background on NLP, describing some of the fundamental problems in processing text like ambiguity and the contextual nature of language. It then outlines several common NLP tools and techniques used to analyze text at different levels, from part-of-speech tagging to named entity recognition and information extraction. The document concludes that NLP can help address some of the limitations of traditional document retrieval models by identifying implicit meanings and relationships within text.
Feature Selection for Document RankingAndrea Gigli
Feature selection for Machine Learning applied to Document Ranking (aka L2R, LtR, LETOR). Contains empirical results on Yahoo! and Bing public available Web Search Engine data.
King Acrisius was warned his daughter Danae's son would kill him, so he imprisoned her. Zeus impregnated Danae, and she gave birth to Perseus. Perseus grew up and performed heroic feats, including slaying the Gorgon Medusa. Andromeda was promised in marriage to a sea monster but was saved by Perseus. The characters are connected through familial relationships, with Perseus's origins tying back to King Acrisius's prophecy and desire to avoid being killed by his grandson.
Pattern recognition and Machine Learning.Rohit Kumar
Machine learning involves using examples to generate a program or model that can classify new examples. It is useful for tasks like recognizing patterns, generating patterns, and predicting outcomes. Some common applications of machine learning include optical character recognition, biometrics, medical diagnosis, and information retrieval. The goal of machine learning is to build models that can recognize patterns in data and make predictions.
International Journal of Engineering Research and Development (IJERD)IJERD Editor
International Journal of Engineering Research and Development is an international premier peer reviewed open access engineering and technology journal promoting the discovery, innovation, advancement and dissemination of basic and transitional knowledge in engineering, technology and related disciplines.
Discovering Novel Information with sentence Level clustering From Multi-docu...irjes
The document presents a novel fuzzy clustering algorithm called FRECCA that clusters sentences from multi-documents to discover new information. FRECCA uses fuzzy relational eigenvector centrality to calculate page rank scores for sentences within clusters, treating the scores as likelihoods. It uses expectation maximization to optimize cluster membership values and mixing coefficients without a parameterized likelihood function. An evaluation shows FRECCA achieves superior performance to other clustering algorithms on a quotations dataset, identifying overlapping clusters of semantically related sentences.
Latent dirichletallocation presentationSoojung Hong
Latent Dirichlet Allocation (LDA) is a generative probabilistic model for collections of discrete data such as text corpora. LDA represents documents as random mixtures over latent topics, characterized by probability distributions over words. LDA addresses limitations of previous topic models like pLSI by treating topic mixtures as random variables rather than document-specific parameters. Variational inference and EM algorithms are used for parameter estimation in LDA. Empirical results show LDA outperforms other models on tasks like document modeling, classification, and collaborative filtering.
AN EMPIRICAL STUDY OF WORD SENSE DISAMBIGUATIONijnlc
Word Sense Disambiguation (WSD) is an important area which has an impact on improving the performance of applications of computational linguistics such as machine translation, information
retrieval, text summarization, question answering systems, etc. We have presented a brief history of WSD,
discussed the Supervised, Unsupervised, and Knowledge-based approaches for WSD. Though many WSD
algorithms exist, we have considered optimal and portable WSD algorithms as most appropriate since they
can be embedded easily in applications of computational linguistics. This paper will also provide an idea of
some of the WSD algorithms and their performances, which compares and assess the need of the word
sense disambiguation.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans. In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
THE ABILITY OF WORD EMBEDDINGS TO CAPTURE WORD SIMILARITIESkevig
Distributed language representation has become the most widely used technique for language representation in various natural language processing tasks. Most of the natural language processing models that are based on deep learning techniques use already pre-trained distributed word representations, commonly called word embeddings. Determining the most qualitative word embeddings is of crucial importance for such models. However, selecting the appropriate word embeddings is a perplexing task since the projected embedding space is not intuitive to humans.In this paper, we explore different approaches for creating distributed word representations. We perform an intrinsic evaluation of several state-of-the-art word embedding methods. Their performance on capturing word similarities is analysed with existing benchmark datasets for word pairs similarities. The research in this paper conducts a correlation analysis between ground truth word similarities and similarities obtained by different word embedding methods.
This document discusses probabilistic models used for text mining. It introduces mixture models, Bayesian nonparametric models, and graphical models including Bayesian networks, hidden Markov models, Markov random fields, and conditional random fields. It provides details on the general framework of mixture models and examples like topic models PLSA and LDA. It also discusses learning algorithms for probabilistic models like EM algorithm and Gibbs sampling.
Document Author Classification Using Parsed Language Structurekevig
Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the
text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used,
for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern
times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of
using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship
using grammatical structural information extracted using a statistical natural language parser. This paper provides a
proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist
Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted
of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the
features into a lower dimensional space. Statistical experiments on these documents demonstrate that information
from a statistical parser can, in fact, assist in distinguishing authors.
Document Author Classification using Parsed Language Structurekevig
Over the years there has been ongoing interest in detecting authorship of a text based on statistical properties of the text, such as by using occurrence rates of noncontextual words. In previous work, these techniques have been used, for example, to determine authorship of all of The Federalist Papers. Such methods may be useful in more modern times to detect fake or AI authorship. Progress in statistical natural language parsers introduces the possibility of using grammatical structure to detect authorship. In this paper we explore a new possibility for detecting authorship using grammatical structural information extracted using a statistical natural language parser. This paper provides a proof of concept, testing author classification based on grammatical structure on a set of “proof texts,” The Federalist Papers and Sanditon which have been as test cases in previous authorship detection studies. Several features extracted from the statisticalnaturallanguage parserwere explored: all subtrees of some depth from any level; rooted subtrees of some depth, part of speech, and part of speech by level in the parse tree. It was found to be helpful to project the features into a lower dimensional space. Statistical experiments on these documents demonstrate that information from a statistical parser can, in fact, assist in distinguishing authors.
Textual information in this new era, it is difficult to manually extract the summary of a large data different areas of
social communication accumulates the enormous amounts of data. Therefore, it is important to develop methods for
searching and absorbing relevant information, selecting important sentences, paragraphs from large texts, to summarize
texts by finding topics of the text and frequency based clustering of sentences. In this paper, the author presents some
ideas on using mathematical models in presenting the source text into a shorter version with semantics, graph-based
approach for text summarization in Mongolian language.
The document discusses text summarization techniques for Mongolian language texts. It proposes using graph-based and matrix-based approaches to model the semantic structure and relationships within texts. Keywords, sentences, and paragraphs can be represented as vertices in a graph. This graph can then be converted into an adjacency matrix to analyze similarities and apply techniques like singular value decomposition to extract important topics and reduce the text. Representing texts as vectors and using techniques like TF-IDF, cosine similarity, and binary/one-hot encoding of keywords is also discussed for automatic text summarization in Mongolian.
Cooperating Techniques for Extracting Conceptual Taxonomies from TextFulvio Rotella
The document proposes a mixed approach using existing natural language processing techniques and novel techniques to automatically construct conceptual taxonomies from text. It identifies relevant concepts from text using keyword extraction, clustering, and computing relevance weights. It then generalizes similar concepts using WordNet to group concepts and disambiguate word senses. Preliminary evaluations show promising initial results.
This document describes a study that analyzed features for a supervised transition-based dependency parser on the Latin Dependency Treebank. It found that using part-of-speech and case features achieved the highest accuracy. The corpus and parsing approach are described, including how dependency graphs are encoded and the transition system used to parse sentences. Projective and non-projective graphs are distinguished, and roughly half the sentences in the corpus exhibited non-projective structures.
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.
Models of Parsing: Two-Stage Models
Models of Parsing: Constraint-Based Models
Story context effects
Subcategory frequency effects
Cross-linguistic frequency data
Semantic effects
Prosody
Visual context effects
Interim Summary
Argument Structure Hypothesis
Limitations, Criticisms, and Some Alternative Parsing Theories
Construal
Race-based parsing
Good-enough parsing
Parsing Long-Distance
Dependencies
Summary and Conclusions
Test Yourself
When people speak, they produce sequences of words. When people listen or read, they also deal with sequences of words. Speakers systematically organize those sequences of words into phrases, clauses, and sentences.
The study of syntax involves discovering the cues that languages provide that show how words in sentences relate to one another.
The study of syntactic parsing involves discovering how comprehenders use those cues to determine how words in sentences relate to one another during the process of interpreting sentence.
Parsing means to breaking down a sentence into its component parts so that the meaning of the sentence can be understood.
This can either be the category of words (Nouns, Pronouns, verbs, adjectives. Etc.)
Or other elements such as verbs tense (present, past, future)
In a phrase structure tree, the labels, like NP, VP, and S, are called nodes and the connections between the different nodes form branches.
The patterns of nodes and branches show how the words in the sentence are grouped together to form phrases and clauses.
The document describes the Correlated Topic Model (CTM), which addresses a limitation of LDA and other topic models by directly modeling correlations between topics. CTM uses a logistic normal distribution over topic proportions instead of a Dirichlet, allowing for covariance structure between topics. This provides a more realistic model of latent topic structure where presence of one topic may be correlated with another. Variational inference is used to approximate posterior inference in CTM. The model is shown to provide a better fit than LDA on a corpus of journal articles.
Extractive Document Summarization - An Unsupervised ApproachFindwise
1. This paper presents and evaluates an unsupervised extractive document summarization system that uses TextRank, K-means clustering, and one-class SVM algorithms for sentence ranking.
2. The system achieves state-of-the-art performance on the DUC 2002 English dataset with a ROUGE score of 0.4797 and can also summarize Swedish documents.
3. Domain knowledge is added through sentence boosting to improve summarization of news articles, and similarities between sentences are calculated to avoid redundancy for multi-document summarization.
[Emnlp] what is glo ve part ii - towards data scienceNikhil Jaiswal
GloVe is a new model for learning word embeddings from co-occurrence matrices that combines elements of global matrix factorization and local context window methods. It trains on the nonzero elements in a word-word co-occurrence matrix rather than the entire sparse matrix or individual context windows. This allows it to efficiently leverage statistical information from the corpus. The model produces a vector space with meaningful structure, as shown by its performance of 75% on a word analogy task. It outperforms related models on similarity tasks and named entity recognition. The full paper describes GloVe's global log-bilinear regression model and how it addresses drawbacks of previous models to encode linear directions of meaning in the vector space.
Phonetic Recognition In Words For Persian Text To Speech Systemspaperpublications3
Abstract:The interest in text to speech synthesis increased in the world .text to speech have been developed for many popular languages such as English, Spanish and French and many researches and developments have been applied to those languages. Persian on the other hand, has been given little attention compared to other languages of similar importance and the research in Persian is still in its infancy. Persian languages possess many difficulty and exceptions that increase complexity of text to speech systems. For example: short vowels is absent in written text or existence of homograph words. in this paper we propose a new method for Persian text to phonetic that base on pronunciations by analogy in words, semantic relations and grammatical rules for finding proper phonetic.Keywords:PbA, text to speech, Persian language, Phonetic recognition.
Title:Phonetic Recognition In Words For Persian Text To Speech Systems
Author:Ahmad Musavi Nasab, Ali Joharpour
International Journal of Recent Research in Mathematics Computer Science and Information Technology (IJRRMCSIT)
Paper Publications
A Neural Probabilistic Language Model.pptx
Bengio, Yoshua, et al. "A neural probabilistic language model." Journal of machine learning research 3.Feb (2003): 1137-1155.
A goal of statistical language modeling is to learn the joint probability function of sequences of
words in a language. This is intrinsically difficult because of the curse of dimensionality: a word
sequence on which the model will be tested is likely to be different from all the word sequences seen
during training. Traditional but very successful approaches based on n-grams obtain generalization
by concatenating very short overlapping sequences seen in the training set. We propose to fight the
curse of dimensionality by learning a distributed representation for words which allows each
training sentence to inform the model about an exponential number of semantically neighboring
sentences. The model learns simultaneously (1) a distributed representation for each word along
with (2) the probability function for word sequences, expressed in terms of these representations.
Generalization is obtained because a sequence of words that has never been seen before gets high
probability if it is made of words that are similar (in the sense of having a nearby representation) to
words forming an already seen sentence. Training such large models (with millions of parameters)
within a reasonable time is itself a significant challenge. We report on experiments using neural
networks for the probability function, showing on two text corpora that the proposed approach
significantly improves on state-of-the-art n-gram models, and that the proposed approach allows to
take advantage of longer contexts.
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Learning to summarize using coherence
1. Proceedings NIPS Workshop on Applications for Topic Models: Text and
Beyond: Dec 2009, Whistler, Canada
Learning to Summarize using Coherence
Pradipto Das Rohini Srihari
Department of Computer Science Department of Computer Science
University at Buffalo University at Buffalo
Buffalo, NY 14260 Buffalo, NY 14260
pdas3@buffalo.edu rohini@cedar.buffalo.edu
Abstract
The focus of our paper is to attempt to define a generative probabilistic topic model
for text summarization that aims at extracting a small subset of sentences from the
corpus with respect to some given query. We theorize that in addition to a bag of
words, a document can also be viewed in a different manner. Words in a sentence
always carry syntactic and semantic information and often such information (for
e.g., the grammatical and semantic role (GSR) of a word like subject, object, noun
and verb concepts etc.) is carried across adjacent sentences to enhance coherence
in different parts of a document. We define a topic model that models documents
by factoring in the GSR transitions for coherence and for a particular query, we
rank sentences by a product of thematical salience and coherence through GSR
transitions.
1 Introduction
Automatic summarization is one of the oldest studied problems in IR and NLP and is still receiving
prominent research focus. In this paper, we propose a new joint model of words and sentences for
multi-document summarization that attempts to integrate the coherence as well as the latent themes
of the documents.
In the realm of computational linguistics, there has been a lot of work in Centering Theory including
Grosz et. al. [3]. Their work specifies how discourse interpretation depends on interactions among
speaker intentions, attentional state, and linguistic form. In our context, we could assume a subset
of documents discussing a particular theme to be a discourse. Attentional state models the discourse
participants’ focus of attention at any given point in the discourse. This focus of attention helps
identify “centers” of utterances that relate different parts of local discourse segments meaningfully
and according to [3], the “centers” are semantic objects, not words, phrases, or syntactic forms
and centering theory helps formalize the constraints on the centers to maximize coherence. In our
context, the GSRs approximate the centers.
Essentially, then the propagation of these centers of utterances across utterances helps maintain the
local coherence. It is important to note that this local coherence is responsible for the choice of
words appearing across utterances in a particular discourse segment and helps reduce the inference
load placed upon the hearer (or reader) to understand the foci of attention.
2 Adapting Centering Theory for Summarization
For building a statistical topic model that incorportes GSR transitions (henceforth GSRts) across ut-
terances, we attributed words in a sentence with GSRs like subjects, objects, concepts from WordNet
synset role assignments(wn), adjectives, VerbNet thematic role assignment(vn), adverbs and “other”
(if the feature of the word doesn’t fall into the previous GSR categories). Further if a word in a
1
2. sentence is identified with 2 or more GSRs, only one GSR is chosen based on the left to right de-
scending priority of the categories mentioned. These features (GSRs) were extracted using the text
analytics engine Semantex (http://www.janyainc.com/) . Thus in a window of sentences, there are potentially
(G + 1)2 GSRts for a total of G GSRs with the additional GSR representing a null feature (denoted
by “−−”) as in the word is not found in the contextual sentence. We used anaphora resolution as
offered by Semantex to substitute pronouns with the referent nouns as a preprocessing step. If there
are TG valid GSRts in the corpus, then a sentence is represented as a vector over GSRt counts only
along with a binary vector over the word vocabulary. It must be emphasized that the GSRs are the
output of a separate natural language parsing system.
For further insight, we can construct a matrix consisting of sentences as rows and words as columns;
the entries in the matrix are filled up with a specific GSR for the word in the corresponding sentence
following GSR priorities (in case of multiple occurences of the same word in the same sentence
with different GSRs). Figure 1 shows a slice of such a matrix taken from the TAC2008 dataset
(http://www.nist.gov/tac/tracks/2008/index.html) which contains documents related to events concerning Christian
minorities in Iraq and their current status. Figure 1 suggests, as in [1], that dense columns of the
GSRs indicate potentially salient and coherent sentences (7 and 8 here) that present less inference
load. The words and the GSRs jointly identify the centers in an utterance.
Figure 1: (a) Left: Sentence IDs and the GSRs of the words in them (b) Right: The corresponding
sentences
Note that the count for the GSRt “wn→ −−” for sentenceID 8 is 3 from this snapshot. Inputs to the
model are document specific word ID counts and document-sentence specific GSRt ID counts.
3 The Proposed Method
To describe the document generation process under our proposed “Learning To Summarize” (hence-
forth LeToS), we assume that there are K latent topics and T topic-coupled GSRts associated with
each document; rt is the observed GSRt, wn is the observed word and sp is the observed sentence.
Denote θ k to be the expected number of GSRts per topic; π t to be the expected number of words
and sentences per topic-coupled GSRt in each document. Further denote, zt to be a K dimensional
indicator for θ, vp be the T dimensional indicator for π and yn is an indicator for the same topic-
coupled GSRt proportion as vp , each time a word wn is associated with a particular sentence sp . At
the parameter level, each topic is a multinomial βk over the vocabulary V of words and each topic
is also a multinomial ρk over the GSRts following the implicit relation of GSRts to words within
sentence windows. Each topic-coupled GSRt is also treated as a multinomial Ωt over the total num-
ber U of sentences in the corpus. δ(wn ∈ sp ) is the delta function which is 1 iff the nth word
belong to the pth sentence. The document generation process is shown in Fig. 3 and is explained as
a pseudocode in Fig. 2.
The model can be viewed as a generative process that first generates the GSRts and subsequently
generates the words that describe the GSRt and hence an utterance unit (a sentence in this model).
For each document, we first generate GSRts using a simple LDA model and then for each of the
Nd words, a GSRt is chosen and a word wn is drawn conditioned on the same factor that generated
the chosen GSRt. Instead of influencing the choice of the GSRt to be selected from an assumed
distribution (e.g. uniform or poisson) of the number of GSRts, the document specific topic-coupled
proportions are used. Finally the sentences are sampled from Ωt by choosing a GSRt proportion
that is coupled to the factor that generates rt through the constituent wn . In disjunction, π along
with vp , sp and Ω focus mainly on coherence among the coarser units - the sentences. However, the
influence of a particular GSRt like “subj→subj” on coherence may be discounted if that is not the
2
3. For each document d ∈ 1, ..., M
Choose a topic proportion θ|α ∼ Dir(α)
Choose topic indicator zt |θ ∼ M ult(θ)
Choose a GSRt rt |zt = k, ρ ∼ M ult(ρzt )
Choose a GSRt proportion π|η ∼ Dir(η)
For each position n in document d:
For each instance of utterance sp for which wn
occurs in sp in document d:
Choose vp |π ∼ M ult(π)
Choose yn ∼ vp δ(wn ∈ sp )
Choose a sentence sp ∼ M ult(Ωvp )
Choose a word wn |yn = t, z, β ∼ M ult(β zy )
n
Figure 2: Document generation process of LeToS Figure 3: Graphical model representation of LeToS
dominant trend in the transition topic. This fact is enforced through the coupling of empirical GSRt
proportions to topics of the sentential words.
3.1 Parameter Estimation and Inference
In this paper we have resorted to mean field variational inference [2] to find as tight as possible an
approximation to the log likelihood of the data (the joint distribution of the observed variables given
the parameters) by minimizing the KL divergence of approximate factorized mean field distribution
to the posterior distribution of the latent variables given the data. In the variational setting, for
K T T
each document we have k=1 φtk = 1, t=1 λnt = 1 and t=1 ζpt = 1 and the approximating
distribution is factorized as:
T N
P
q(θ, π, z, y, v|γ, χ, φ, λ, ζ) = q(θ|γ)q(π|χ) q(zt |φt ) q(wn |λn ) q(sp |ζp ) (1)
t=1 n=1 p=1
The variational functional to optimize can be shown to be
F = Eq [log p(r, w, s|α, θ, η, π, ρ, β, Ω)] − Eq [log q(θ, π, z, y|γ, χ, φ, λ, ζ)] (2)
where Eq [f (.)] is the expectation of f (.) under the q distribution.
The maximum likelihood estimations of these indicator variables for the topics and the topic-coupled
GSRts are as follows:
Td Nd Pd
γi = αi + t=1 φti ; χt = ηt + n=1 λnt + p=1 ζpt
T K
λnt ∝ exp{(Ψ(χt ) − Ψ( f =1 χf )) + ( i=1 φti log βz(yn =t) =i,n )}
K Nd
φti ∝ exp{log ρit + (Ψ(γi ) − Ψ( k=1 γk ))+( n=1 λnt log βz(yn =t) =i,n )}
T
ζpt ∝ Ωpt exp{Ψ(χt ) − Ψ( j=1 χj )}
We now write the expressions for the maximum likelihood of the parameters of the original graphical
model using derivatives w.r.t the parameters of the functional F in Equ. (2). We have the following
results:
M Td g M Nd Td j
ρig ∝ d=1 t=1 φdti rdt ; βij ∝ d=1 n=1 ( t=1 λnt φti )wdn ;
M Pd
Ωtu ∝ d=1 p=1 ζdpt su dp
g
where rdt is 1 iff t = g and 0 otherwise with g as an index variable for all possible GSRts; u is an
index into one of the U sentences in the corpus and su = 1 if the pth sentence in document d is one
dp
among U . The updates of α and η are exactly the same as mentioned in [2].
For obtaining summaries, we order sentences w.r.t query words by computing the following:
Q
T K
p(sdp |wq ) ∝ ( ζdpt φdti (λdlt φdti )γdi χdt )δ(wl ∈ sdp ) (3)
l=1 t=1 i=1
where Q is the number of the query words and su is the uth sentence in the corpus that belongs to all
such document ds which are relevant to the query, wl is the lth query word. Further, the sentences
3
4. are scored over only “rich” GSRts which lack any “−− → −−” transitions whenever possible. We
also expand the query by a few words while summarizing in real time using topic inference on the
relevant set of documents.
4 Results and Discussions
Tables 1 and 2 show some
topics learnt from the TAC2009 dataset
. From table 2, we observe that the topics under
(http://www.nist.gov/tac/2009/Summarization/index.html)
both models are the same qualitatively. Moreover, it has been observed that constraining LeToS to
words and GSRts as the only observed variables shows lower word perplexity than LDA on heldout
test data. Empirically, it has been seen that the time complexity for LeToS is sligthly higher than
LDA due to the extra iterations over the GSRts and sentences.
topic16 topic36 topic38 topic22 topic58 topic1 topic42 topic28
Kozlowski bombings solar Hurricane Kozlowski Malik solar Hurricane
million Malik energy Rita Tyco bombs energy Rita
Tyco Sikhs power evacuations million India power evacuated
company Bagri BP Texas company Sikh electricity storms
trial India company Louisiana loan killing systems Texas
Swartz case year area trial Flight government Louisiana
loans killed panel state Swartz Bagri production area
Table 1: Some topics under LDA for TAC2009 Table 2: Some topics under LeToS for TAC2009
For TAC2009, using the more meaningful Pyramid [4] scoring for summaries, the average Pyramid
scores for very short 100 word summaries over 44 queries were obtained as 0.3024 for the A timeline
and 0.2601 for the B timeline for LeToS and ranked 13th and 9th of 52 submissions. The scores
for a state-of-the-art summarization system [5] that uses coherence to some extent and a baseline
returning all the leading sentences (up to 100 words) in the most recent document are (0.1756 and
0.1601) and (0.175 and 0.160) respectively for the A and B timelines. The score for the B timeline
is lower due to redundancy.
5 Conclusion
Overall, we have integrated centering theory based coherence into topic model. Models like LeToS
tend to capture “what is being discussed” by selecting sentences that have low reader “inference
load”. On the other hand, the model gets penalized if the summaries need to be very factual. This
could probably be avoided by defining finer GSR categories such as named entities. Another draw-
back of the model is its lack of understanding the meaning of the query. However, generating
specific summaries w.r.t. an information need using topic modeling is akin to answering natural
language questions. That problem is hard, albeit an open one under the topic modeling umbrella.
References
[1] Regina Barzilay and Mirella Lapata. Modeling local coherence: an entity-based approach. In
ACL ’05: Proceedings of the 43rd Annual Meeting on Association for Computational Linguis-
tics, pages 141–148. Association for Computational Linguistics, 2005.
[2] David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent dirichlet allocation. Journal of
Machine Learning Research, 3:993–1022, 2003.
[3] Barbara J. Grosz, Scott Weinstein, and Arvind K. Joshi. Centering: A framework for modeling
the local coherence of discourse. In Computational Linguistics, volume 21, pages 203–225,
1995.
[4] Aaron Harnly, Ani Nenkova, Rebecca Passonneau, and Owen Rambow. Automation of sum-
mary evaluation by the pyramid method. In Recent Advances in Natural Language Processing
(RANLP), 2005.
[5] Rohini Srihari, Li Xu, and Tushar Saxena. Use of ranked cross document evidence trails for
hypothesis generation. In Proceedings of the 13th International Conference on Knowledge
Discovery and Data Mining (KDD), pages 677–686, San Jose, CA, 2007.
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