1. Articles in category: WSD

    49-72 of 353 « 1 2 3 4 5 6 ... 13 14 15 »
    1. 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
    2. 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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    3. 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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    4. 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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    5. 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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    6. 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
    7. 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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    8. 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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    9. Methods for Automatic WSD

      Research in automatic word sense disambiguation has a long history on a par with computational linguistics itself. In this chapter, we take a two-dimensional approach to review the development and state of the art of the field, by the knowledge sources used for disambiguation on the one hand, and the algorithmic mechanisms with which the knowledge sources are actually deployed on the other. The trend for the latter is relatively clear, correlating closely with the historical development of many other natural language processing subtasks, where conventional knowledge-based methods gradually give way to scalable, corpus-based statistical and supervised methods. While the ...
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    10. The Psychology of WSD

      How do humans resolve semantically ambiguous words? It happens that we will not find a direct answer from psycholinguistic studies. Nevertheless, through probing the organisation of words in the mental lexicon and the access of words, particularly those with multiple meanings, in the human mind, useful hints might be found. In this chapter, we focus our attention on the cognitive aspects of word sense disambiguation. We first review the psychological findings on the mental lexicon, including the storage of words, the representation of meanings, and sense distinction. Mechanisms of lexical access will then be discussed, especially with reference to the ...
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    11. Sense Concreteness and Lexical Activation

      Psycholinguistic evidence has thus suggested the differential processing of concrete and abstract concepts by the human mind. This chapter further explores the mental lexicon with respect to the concreteness and abstractness of concepts based on word association data. Since lexical resources including computational semantic lexicons play a critical role in automatic word sense disambiguation, we aim at investigating to what extent such concreteness distinction is modelled in existing lexical resources. It was observed that concrete and abstract noun senses tend to exhibit consistently different lexical activation patterns, and the results suggest that sense concreteness may serve as a possible alternative ...
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    12. Lessons Learned from Evaluation

      The performance evaluation of word sense disambiguation systems has only been more or less standardised in the last decade with the first three SENSEVAL and the more recent SEMEVAL exercises. These exercises have pointed to the superiority of supervised methods using multiple knowledge sources and ensembles of classifiers. Behind the apparently plateaued performance of state-of-the-art systems, some fundamental issues including sense granularity, sparseness of sense-tagged data, and contribution to real applications, still remain. But more importantly, evaluation results also suggest that there is something about the target words themselves which is responsible for the differential performance among systems trained on ...
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    13. Lexical Sensitivity of WSD: An Outlook

      We have tried to show from our discussion in the previous chapters that while ensembles of classifiers based on supervised learning methods trained on multiple contextual features have proved to perform superiorly in current mainstream automatic word sense disambiguation, and their performance might have apparently reached a plateau, there are still considerable unknowns as far as the lexical sensitivity of the task is concerned. We have also suggested that these under-explored parts cannot be adequately addressed from the computational perspective alone, as they probably involve some intrinsic properties of words and senses, like concept concreteness, which may be cognitively based ...
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    14. Lexical Functions and Their Applications

      As a concept, lexical function (LF) was introduced in the frame of the Meaning-Text Theory (MTT) presented in (Mel’čuk, 1974, 1996) in order to describe lexical restrictions and preferences of words in choosing their “companions” when expressing certain meanings in text. Here we will give a brief account of the fundamental concepts and statements of MTT as the context of LFs. Actually, the formalism of lexical functions has been one of those parts of MTT which attracted most attention of specialists in general linguistics and, in particular, computational linguistics. A lot of research began in the area of natural ...
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    15. Performance Analysis of Case Based Word Sense Disambiguation with Minimal Features Using Neural Network

      In this paper, the performance of case based word sense disambiguation attained by two different set of knowledge features, such as bigram and trigram are analyzed for identifying the best for word sense disambiguation. To uncover the ambiguous of a word, so many knowledge features like part of features (PoS), collocation, bag of words, noun-verb relation etc. Here, ambiguity of a word is removed with only two and three elements referred as bigram and trigram. Two different representations of bigram, pre-bigram and post-bigram and three different forms of trigram, pre-trigram, in-trigram and post-trigram are considered for disambiguation. Relevant knowledge features ...
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    16. Semantic Similarity Functions in Word Sense Disambiguation

      This paper presents a method of improving the results of automatic Word Sense Disambiguation by generalizing nouns appearing in a disambiguated context to concepts. A corpus-based semantic similarity function is used for that purpose, by substituting appearances of particular nouns with a set of the most closely related similar words. We show that this approach may be applied to both supervised and unsupervised WSD methods and in both cases leads to an improvement in disambiguation accuracy. We evaluate the proposed approach by conducting a series of lexical sample WSD experiments on both domain-restricted dataset and a general, balanced Polish-language text ...
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    17. An Efficient Feature Frequency-Based Approach to Tackle Cross-Lingual Word Sense Disambiguation

      The Cross-Lingual Word Sense Disambiguation (CLWSD) problem is a challenging Natural Language Processing (NLP) task that consists of selecting the correct translation of an ambiguous word in a given context. Different approaches have been proposed to tackle this problem, but they are often complex and need tuning and parameter optimization. In this paper, we propose a new classifier, Selected Binary Feature Combination (SBFC), for the CLWSD problem. The underlying hypothesis of SBFC is that a translation is a good classification label for new instances if the features that occur frequently in the new instance also occur frequently in the training ...
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    18. Kannada Word Sense Disambiguation Using Association Rules

      Disambiguating the polysemous word is one of the major issues in the process of Machine Translation. The word may have many senses, selecting the most appropriate sense for an ambiguous word in a sentence is a central problem in Machine Translation. Because, each sense of a word in a source language sentence may generate different target language sentences. Knowledge and corpus based methods are usually applied for disambiguation task. In the present paper, we propose an algorithm to disambiguate Kannada polysemous words using association rules. We built Kannada corpora using web resources. The corpora are divided in to training and ...
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    19. An Automatic Approach for Mapping Product Taxonomies in E-Commerce Systems

      The recent explosion of Web shops has made the user task of finding the desired products an increasingly difficult one. One way to solve this problem is to offer an integrated access to product information on the Web, for which an important component is the mapping of product taxonomies. In this paper, we introduce CMAP, an algorithm that can be used to map one product taxonomy to another product taxonomy. CMAP employs word sense disambiguation techniques and lexical and structural similarity measures in order to find the best matching categories. The performance on precision, recall, and the F 1-measure is ...
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    20. An Automated Approach to Product Taxonomy Mapping in E-Commerce

      Due to the ever-growing amount of information available on Web shops, it has become increasingly difficult to get an overview of Web-based product information. There are clear indications that better search capabilities, such as the exploitation of annotated data, are needed to keep online shopping transparent for the user. For example, annotations can help present information from multiple sources in a uniform manner. This paper proposes an algorithm that can autonomously map heterogeneous product taxonomies forWeb shop data integration purposes. The proposed approach uses word sense disambiguation techniques, approximate lexical matching, and a mechanism that deals with composite categories. Our ...
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    21. A Linguistic Approach for Semantic Web Service Discovery

      We propose a Semantic Web Service Discovery framework for finding semantically annotated Web services by using natural language processing techniques. The framework searches through a set of annotated Web services for matches with a user query, which consists of keywords, so that knowledge about semantic languages is not required. For matching keywords with Semantic Web service descriptions given in Web Service Modeling Ontology (WSMO), techniques like part-of-speech tagging, lemmatization, and word sense disambiguation are used. Three different matching algorithms are defined and evaluated for their ability to do exact matching and approximate matching between the user query and Web Service ...
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    22. Applying Deep Belief Networks to Word Sense Disambiguation. (arXiv:1207.0396v1 [cs.CL])

      In this paper, we applied a novel learning algorithm, namely, Deep Belief Networks (DBN) to word sense disambiguation (WSD). DBN is a probabilistic generative model composed of multiple layers of hidden units. DBN uses Restricted Boltzmann Machine (RBM) to greedily train layer by layer as a pretraining. Then, a separate fine tuning step is employed to improve the discriminative power. We compared DBN with various state-of-the-art supervised learning algorithms in WSD such as Support Vector Machine (SVM), Maximum Entropy model (MaxEnt), Naive Bayes classifier (NB) and Kernel Principal Component Analysis (KPCA). We used all words in the given paragraph, surrounding ...
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      Mentions: Bayes WSD Kpca
    23. Word sense disambiguation using emergent categories

      Disclosed herein is a computer implemented method and system for word sense disambiguation in a natural language sentence. The natural language sentence is parsed for identifying possible parts of speech for each term and identifying possible phrase structures. Terms comprising one or more linguistic roles are identified. The possible sense combinations for the terms with linguistic roles are identified. Emergent categories are applied to identify possible valid senses for each of the terms with identified linguistic roles. Linguistic role pairs are identified from among the terms identified with linguistic roles. The correspondence functions with the correspondence function types matching the ...
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    24. Word Sense Disambiguation as an Integer Linear Programming Problem

      We present an integer linear programming model of word sense disambiguation. Given a sentence, an inventory of possible senses per word, and a sense relatedness measure, the model assigns to the sentence’s word occurrences the senses that maximize the total pairwise sense relatedness. Experimental results show that our model, with two unsupervised sense relatedness measures, compares well against two other prominent unsupervised word sense disambiguation methods. Content Type Book ChapterPages 33-40DOI 10.1007/978-3-642-30448-4_5Authors Vicky Panagiotopoulou, Department of Informatics, Athens University of Economics and Business, GreeceIraklis Varlamis, Department of Informatics and Telematics, Harokopio University, Athens, GreeceIon Androutsopoulos, Department of ...
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    49-72 of 353 « 1 2 3 4 5 6 ... 13 14 15 »
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