1. Articles in category: WSD

    49-72 of 369 « 1 2 3 4 5 6 ... 14 15 16 »
    1. Hyperdimensional computing approach to word sense disambiguation.

      Hyperdimensional computing approach to word sense disambiguation. AMIA Annu Symp Proc. 2012;2012:1129-38 Authors: Berster BT, Goodwin JC, Cohen T Abstract Coping with the ambiguous meanings of words has long been a hurdle for information retrieval and natural language processing systems. This paper presents a new word sense disambiguation approach using high-dimensional binary vectors, which encode meanings of words based on the different contexts in which they occur. In our approach, a randomly constructed vector is assigned to each ambiguous term, and another to each sense of this term. In the context of a sense-annotated training set, a reversible ...
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    2. Method, system and apparatus for automatic keyword extraction

      The present invention provides a method and a system for automatic keyword extraction based on supervised or unsupervised machine learning techniques. Novel linguistically-motivated machine learning features are introduced, including discourse comprehension features based on construction integration theory, numeric features making use of syntactic part-of-speech patterns, and probabilistic features based on analysis of online encyclopedia annotations. The improved keyword extraction methods are combined with word sense disambiguation into a system for automatically generating annotations to enrich text with links to encyclopedic knowledge.
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    3. The cross-lingual lexical substitution task

      The cross-lingual lexical substitution task
      Abstract  In this paper we provide an account of the cross-lingual lexical substitution task run as part of SemEval-2010. In this task both annotators (native Spanish speakers, proficient in English) and participating systems had to find Spanish translations for target words in the context of an English sentence. Because only translations of a single lexical unit were required, this task does not necessitate a full blown translation system. This we hope encouraged those working specifically on lexical semantics to participate without a requirement for them to use machine translation software, though they were free to use whatever resources they chose ...
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    4. Multi-view constrained clustering with an incomplete mapping between views

      Multi-view constrained clustering with an incomplete mapping between views
      Abstract  Multi-view learning algorithms typically assume a complete bipartite mapping between the different views in order to exchange information during the learning process. However, many applications provide only a partial mapping between the views, creating a challenge for current methods. To address this problem, we propose a multi-view algorithm based on constrained clustering that can operate with an incomplete mapping. Given a set of pairwise constraints in each view, our approach propagates these constraints using a local similarity measure to those instances that can be mapped to the other views, allowing the propagated constraints to be transferred across views via ...
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    5. A Classification Model with Corpus Enrichment for Toponym Disambiguation

      This paper presents a method based on information retrieval to enrich corpus using bootstrapping techniques. A supervised corpus manually validated is provided, and then snippets are obtained from Web in order to increase the size of the initial corpus. Although this technique has already been reported in the literature, the main objective of this work is to apply it under the specific task of GEO/NO-GEO toponym disambiguation.The disambiguation procedure is evaluated by a classification model observing favorable results. Content Type Book ChapterPages 472-480DOI 10.1007/978-3-642-34654-5_48Authors Belém Priego Sánchez, FCC, Benemérita Universidad Autónoma de Puebla, Av. San Claudio ...
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    6. N-Gram Features for Unsupervised WSD with an Underlying Naïve Bayes Model

      The feature selection method we are presenting in this chapter relies on web scale N-gram counts. It uses counts collected from the web in order to rank candidates. Features are thus created from unlabeled data, a strategy which is part of a growing trend in natural language processing. Disambiguation results obtained by web N-gram feature selection will be compared to those of previous approaches that equally rely on an underlying Naïve Bayes model but on completely different feature sets. Test results corresponding to the main parts of speech (nouns, adjectives, verbs) will show that web N-gram feature selection for the ...
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    7. Preliminaries

      Preliminaries
      This chapter describes the problem we are investigating and trying to solve in all other chapters. It introduces word sense disambiguation (WSD) and Naïve Bayes-based WSD, as well as local type features for unsupervised WSD with an underlying Naïve Bayes model. Content Type Book ChapterPages 1-8DOI 10.1007/978-3-642-33693-5_1Authors Florentina T. Hristea, Faculty of Mathematics and Computer Science, Department of Computer Science, University of Bucharest, Emil Racovita 12, 041758 Bucharest, Romania Book Series SpringerBriefs in StatisticsOnline ISSN 2191-5458Print ISSN 2191-544X Book The Naïve Bayes Model for Unsupervised Word Sense DisambiguationDOI 10.1007/978-3-642-33693-5Online ISBN 978-3-642-33693-5Print ISBN 978-3-642-33692-8
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    8. N-Gram Features for Unsupervised WSD with an Underlying Naïve Bayes Model

      N-Gram Features for Unsupervised WSD with an Underlying Naïve Bayes Model
      The feature selection method we are presenting in this chapter relies on web scale N-gram counts. It uses counts collected from the web in order to rank candidates. Features are thus created from unlabeled data, a strategy which is part of a growing trend in natural language processing. Disambiguation results obtained by web N-gram feature selection will be compared to those of previous approaches that equally rely on an underlying Naïve Bayes model but on completely different feature sets. Test results corresponding to the main parts of speech (nouns, adjectives, verbs) will show that web N-gram feature selection for the ...
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    9. Semantic WordNet-Based Feature Selection

      The feature selection method we are presenting in this chapter makes use of the semantic network WordNet as knowledge source for feature selection. The method makes ample use of the WordNet semantic relations which are typical of each part of speech, thus placing the disambiguation process at the border between unsupervised and knowledge-based techniques. Test results corresponding to the main parts of speech (nouns, adjectives, verbs) will be compared to previously existing disambiguation results, obtained when performing a completely different type of feature selection. Our main conclusion will be that the Naïve Bayes model reacts well in the presence of ...
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    10. Syntactic Dependency-Based Feature Selection

      The feature selection method we are presenting in this chapter makes use of syntactic knowledge provided by dependency relations. Dependency-based feature selection for the Naïve Bayes model is examined and exemplified in the case of adjectives. Performing this type of knowledge-based feature selection places the disambiguation process at the border between unsupervised and knowledge-based techniques. The discussed type of feature selection and corresponding disambiguation method will once again prove that a basic, simple knowledge-lean disambiguation algorithm, hereby represented by the Naïve Bayes model, can perform quite well when provided knowledge in an appropriate way. Our main conclusion will be that ...
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    11. The Naïve Bayes Model for Unsupervised Word Sense Disambiguation

      The Naïve Bayes Model for Unsupervised Word Sense Disambiguation Content Type BookPublisher Springer Berlin HeidelbergDOI 10.1007/978-3-642-33693-5Copyright 2013ISBN 978-3-642-33692-8 (Print) 978-3-642-33693-5 (Online)Authors Florentina T. Hristea, Faculty of Mathematics & Computer Scienc, Department of Computer Science, University of Bucharest, Emil Racovita 12, Bucharest, 041758 Romania Book Series SpringerBriefs in StatisticsOnline ISSN 2191-5458Print ISSN 2191-544X
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    12. The Naïve Bayes Model in the Context of Word Sense Disambiguation

      This chapter discusses the Naïve Bayes model strictly in the context of word sense disambiguation. The theoretical model is presented and its implementation is discussed. Special attention is paid to parameter estimation and to feature selection, the two main issues of the model’s implementation. The EM algorithm is recommended as suitable for parameter estimation in the case of unsupervised WSD. Feature selection will be surveyed in the following chapters. Content Type Book ChapterPages 9-16DOI 10.1007/978-3-642-33693-5_2Authors Florentina T. Hristea, Faculty of Mathematics and Computer Science, Department of Computer Science, University of Bucharest, Emil Racovita 12, 041758 Bucharest, Romania ...
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    13. Fextor: A Feature Extraction Framework for Natural Language Processing: A Case Study in Word Sense Disambiguation, Relation Recognition and Anaphora Resolution

      Feature extraction from text corpora is an important step in Natural Language Processing (NLP), especially for Machine Learning (ML) techniques. Various NLP tasks have many common steps, e.g. low level act of reading a corpus and obtaining text windows from it. Some high-level processing steps might also be shared, e.g. testing for morpho-syntactic constraints between words. An integrated feature extraction framework removes wasteful redundancy and helps in rapid prototyping. In this paper we present a flexible feature extraction framework called Fextor. We describe assumptions about the feature extraction process and provide general overview of software architecture. This is ...
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    14. Annotating Words Using WordNet Semantic Glosses

      An approach to the word sense disambiguation (WSD) relaying on the WordNet synsets is proposed. The method uses semantically tagged glosses to perform a process similar to the spreading activation in semantic network, creating ranking of the most probable meanings for word annotation. Preliminary evaluation shows quite promising results. Comparison with the state-of-the-art WSD methods indicates that the use of WordNet relations and semantically tagged glosses should enhance accuracy of word disambiguation methods. Content Type Book ChapterPages 180-187DOI 10.1007/978-3-642-34478-7_23Authors Julian Szymański, Department of Computer Systems Architecture, Gdańsk University of Technology, PolandWłodzisław Duch, Department of Informatics, Nicolaus Copernicus University ...
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    15. Word Sense Disambiguation Based on Example Sentences in Dictionary and Automatically Acquired from Parallel Corpus

      This paper presents a precision oriented example based approach for word sense disambiguation (WSD) for a reading assistant system for Japanese learners. Our WSD classifier chooses a sense associated with the most similar sentence in a dictionary only if the similarity is high enough, otherwise chooses no sense. We propose sentence similarity measures by exploiting collocations and syntactic dependency relations for a target word. The example based classifier is combined with a Robinson classifier to compensate recall. We further improve WSD performance by automatically acquiring bilingual sentences from a parallel corpus. According to the results of our experiments, the accuracy ...
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      Mentions: Japan WSD
    16. Constrained Log-Likelihood-Based Semi-supervised Linear Discriminant Analysis

      A novel approach to semi-supervised learning for classical Fisher linear discriminant analysis is presented. It formulates the problem in terms of a constrained log-likelihood approach, where the semi-supervision comes in through the constraints. These constraints encode that the parameters in linear discriminant analysis fulfill particular relations involving label-dependent and label-independent quantities. In this way, the latter type of parameters, which can be estimated based on unlabeled data, impose constraints on the former. The former parameters are the class-conditional means and the average within-class covariance matrix, which are the parameters of interest in linear discriminant analysis. The constraints lead to a ...
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    17. Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification.

      Knowledge-based biomedical word sense disambiguation: an evaluation and application to clinical document classification. J Am Med Inform Assoc. 2012 Oct 16; Authors: Garla VN, Brandt C Abstract BACKGROUND: Word sense disambiguation (WSD) methods automatically assign an unambiguous concept to an ambiguous term based on context, and are important to many text-processing tasks. In this study we developed and evaluated a knowledge-based WSD method that uses semantic similarity measures derived from the Unified Medical Language System (UMLS) and evaluated the contribution of WSD to clinical text classification. METHODS: We evaluated our system on biomedical WSD datasets and determined the contribution of ...
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    18. Knowledge-Intensive Word Disambiguation via Common-Sense and Wikipedia

      A promising approach to cope with the challenges that Word Sense Disambiguation brings is to use knowledge-intensive methods. Typically they rely on Wikipedia for supporting automatic concept identification. The exclusive use of Wikipedia as a knowledge base for word disambiguation and therefore the general identification of topics, however, have low accuracy vis-à-vis texts with diverse topics, as can be the case with blogs. This motivated us to propose a method for word disambiguation that, in addition to the use of Wikipedia, uses a common sense database. Use of this base enriches the definition of the concepts previously identified with the ...
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    19. Learning word sense disambiguation in biomedical text with difference between training and test distributions.

      Related Articles Learning word sense disambiguation in biomedical text with difference between training and test distributions. Int J Data Min Bioinform. 2012;6(2):216-37 Authors: Son JW, Park SB Abstract Word Sense Disambiguation methods based on machine learning techniques with lexical features suffer from the discordance between distributions of the training and test documents, due to the diversity of lexical space. To tackle this problem, this paper proposes Support Vector Machines with Example-wise Weights. In this method, the training distribution is matched with the test distribution by weighting training examples according to their similarity to all test data. The ...
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    20. Sense Disambiguation Technique for Information Retrieval in Web Search

      Word Sense Disambiguation is the process of removing and resolving the ambiguity between words. One of the major applications of Word Sense Disambiguation (WSD) is Information Retrieval (IR). In Information Retrieval WSD helps in improving term indexing, if the senses are included as index terms. The order, in which the documents appear as the result of some search on the web, should not be based on their page ranks alone. Some other factors should also be considered while ranking the pages. This paper focuses on the technique that will describe how senses of words can play an important role in ...
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      Mentions: India Rajasthan
    21. New Perspectives on Computational and Cognitive Strategies for Word Sense Disambiguation

      New Perspectives on Computational and Cognitive Strategies for Word Sense Disambiguation Content Type BookPublisher Springer New YorkDOI 10.1007/978-1-4614-1320-2Copyright 2013ISBN 978-1-4614-1319-6 (Print) 978-1-4614-1320-2 (Online)Authors Oi Yee Kwong, Department of Chinese, Translation and Linguistics, City University of Hong Kong, Kowloon, Hong Kong, People’s Republic of China Book Series SpringerBriefs in Electrical and Computer EngineeringOnline ISSN 2191-8120Print ISSN 2191-8112
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    22. Word Senses and Problem Definition

      This book is about word sense disambiguation, the process of figuring out word meanings in a discourse which is an essential task in natural language processing. Computational linguists’ efforts over several decades have led to an apparently plateaued performance in state-of-the-art systems, but considerable unknowns regarding the lexical sensitivity of the task still remain. We propose to address this issue through a better synergy between the computational and cognitive paradigms, which had once closely supported and mutually advanced each other. We start off with an introduction to the word sense disambiguation problem and the notion of word senses in this ...
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    49-72 of 369 « 1 2 3 4 5 6 ... 14 15 16 »
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