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Word Sense Disambiguation using WSD specific Wordnet of Polysemy Words  [PDF]
Udaya Raj Dhungana,Subarna Shakya,Kabita Baral,Bharat Sharma
Computer Science , 2014,
Abstract: 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.
Using Machine Learning Algorithms for Word Sense Disambiguation: A Brief Survey  [PDF]
Neetu Sharma,,Samit Kumar, Dr. S. Niranjan
International Journal of Computer Technology and Electronics Engineering , 2012,
Abstract: In the entire vocabulary of Human language, numerous words have more than one distinct meaning and thus present a contextual ambiguity which is a worth of one of the many language based problems needs procedure based resolution. Approaches to WSD are often classified according to the main source of knowledge used in sense differentiation. Methods that rely primarily on dictionaries, thesauri, and lexical knowledge bases, without using any corpus evidence, are termed dictionary-based or knowledge based. Natural language tends to be ambiguous. Comparing and evaluating different WSD systems is extremely difficult, because of the different test sets, sense inventories, and knowledge resources adopted. In this research we shall address the problem of Word Sense Disambiguation by a combination of learning algorithms. The study is aimed at comparing the performance of using machine learning algorithms for Word Sense Disambiguation (WSD)
Word Sense Disambiguation: An Empirical Survey
J. Sreedhar,S. Viswanadha Raju,A. Vinaya Babu,Amjan Shaik
International Journal of Soft Computing & Engineering , 2012,
Abstract: Word Sense Disambiguation(WSD) is a vital area which is very useful in today’s world. Many WSD algorithms are available in literature, we have chosen to opt for an optimal and portable WSD algorithms. We are discussed the supervised, unsupervised, and knowledge-based approaches for WSD. This paper will also furnish an idea of few of the WSD algorithms and their performances, Which compares and asses the need of the word sense disambiguity.
A State of the Art of Word Sense Induction: A Way Towards Word Sense Disambiguation for Under-Resourced Languages  [PDF]
Mohammad Nasiruddin
Computer Science , 2013,
Abstract: Word Sense Disambiguation (WSD), the process of automatically identifying the meaning of a polysemous word in a sentence, is a fundamental task in Natural Language Processing (NLP). Progress in this approach to WSD opens up many promising developments in the field of NLP and its applications. Indeed, improvement over current performance levels could allow us to take a first step towards natural language understanding. Due to the lack of lexical resources it is sometimes difficult to perform WSD for under-resourced languages. This paper is an investigation on how to initiate research in WSD for under-resourced languages by applying Word Sense Induction (WSI) and suggests some interesting topics to focus on.
Research on Unsupervised WSD Method Based on Sense Categories
基于义类的无导词义消歧方法的研究*

QUAN Chang-qin,HE Ting-ting,JI Dong-hong,LIU Hui,
全昌勤
,何婷婷,姬东鸿,刘辉

计算机应用研究 , 2005,
Abstract: Word Sense Disambiguation( WSD) plays an important role in many areas of natural language processing. In order to deal with large-scale WSD, an unsupervised WSD method is provided in this paper. Based on the vector space model, the mapping relationship between word sense and sense categories is set up by using Machine Readable Dictionary ( MRD) and sense categories thesaurus; and then use the knowledge of sense categories to learn disambiguate feature in corpus unsupervis-edly; finally, the disambiguate feature is used to disambiguate ambiguous words.
Word sense disambiguation: a survey  [PDF]
Alok Ranjan Pal,Diganta Saha
Computer Science , 2015, DOI: 10.5121/ijctcm.2015.5301
Abstract: In this paper, we made a survey on Word Sense Disambiguation (WSD). Near about in all major languages around the world, research in WSD has been conducted upto different extents. In this paper, we have gone through a survey regarding the different approaches adopted in different research works, the State of the Art in the performance in this domain, recent works in different Indian languages and finally a survey in Bengali language. We have made a survey on different competitions in this field and the bench mark results, obtained from those competitions.
Boosting Applied to Word Sense Disambiguation  [PDF]
Gerard Escudero,Lluis Marquez,German Rigau
Computer Science , 2000,
Abstract: In this paper Schapire and Singer's AdaBoost.MH boosting algorithm is applied to the Word Sense Disambiguation (WSD) problem. Initial experiments on a set of 15 selected polysemous words show that the boosting approach surpasses Naive Bayes and Exemplar-based approaches, which represent state-of-the-art accuracy on supervised WSD. In order to make boosting practical for a real learning domain of thousands of words, several ways of accelerating the algorithm by reducing the feature space are studied. The best variant, which we call LazyBoosting, is tested on the largest sense-tagged corpus available containing 192,800 examples of the 191 most frequent and ambiguous English words. Again, boosting compares favourably to the other benchmark algorithms.
Improvement in Word Sense Disambiguation by introducing enhancements in English WordNet Structure
Deepesh Kumar Kimtani,Jyotirmayee Choudhury,Alok Chakrabarty
International Journal on Computer Science and Engineering , 2012,
Abstract: Word sense disambiguation (WSD) is an open problem of natural language processing, which governs the process of identifying the appropriate sense of a word (i.e. intended meaning) in a sentence,when the word has multiple meanings. In this paper we introduce a new WordNet database relation structure whose usage enhances the WSD efficiency of knowledge-based contextual overlap dependent WSD algorithms, such as the popular Lesk algorithm. The efficiency of WSD, on the usage of the proposed WordNet over existing WordNet as a knowledge-base, has been experimentally verified by using the Lesk algorithm on a rich collection of heterogeneous sentences. Use of the proposed WordNet for Lesk Algorithm highly increases the chances of contextual overlap, thereby resulting in high accuracy of proper sense or context identification of the words. The WSD results and accuracies, obtained using the proposed WordNet, have been compared with the results obtained using existing WordNet. Experimental results show that use of our proposed WordNet results in better accuracy of WSD than the existing WordNet. Thus its usage will help the users better, in doing Machine translation, which is one of the most difficult problems of natural language processing
Studying the correlation between different word sense disambiguation methods and summarization effectiveness in biomedical texts
Laura Plaza, Antonio J Jimeno-Yepes, Alberto Díaz, Alan R Aronson
BMC Bioinformatics , 2011, DOI: 10.1186/1471-2105-12-355
Abstract: We present three existing knowledge-based WSD approaches and a graph-based summarizer. Both the WSD approaches and the summarizer employ the Unified Medical Language System (UMLS) Metathesaurus as the knowledge source. We first evaluate WSD directly, by comparing the prediction of the WSD methods to two reference sets: the NLM WSD dataset and the MSH WSD collection. We next apply the different WSD methods as part of the summarizer, to map documents onto concepts in the UMLS Metathesaurus, and evaluate the summaries that are generated. The results obtained by the different methods in both evaluations are studied and compared.It has been found that the use of WSD techniques has a positive impact on the results of our graph-based summarizer, and that, when both the WSD and summarization tasks are assessed over large and homogeneous evaluation collections, there exists a correlation between the overall results of the WSD and summarization tasks. Furthermore, the best WSD algorithm in the first task tends to be also the best one in the second. However, we also found that the improvement achieved by the summarizer is not directly correlated with the WSD performance. The most likely reason is that the errors in disambiguation are not equally important but depend on the relative salience of the different concepts in the document to be summarized.Word sense disambiguation (WSD) is an open problem of natural language processing (NLP) aimed at resolving lexical ambiguities by identifying the correct meaning of a word based on its context. A word is ambiguous when it has more than one sense (e.g. the word "cold", when used as a noun, may refer both to a respiratory disorder and to the absence of heat). It is the context in which the word is used that determines its correct meaning.Word sense disambiguation is not an end in itself, but has obvious relationships with nearly every task that implies natural language understanding [1], such as text categorization [2], information ex
Logarithm Model Based Word Sense Disambiguation
基于对数模型的词义自动消歧

ZHU Jing bo,LI Heng,ZHANG Yue,YAO Tian shun,
朱靖波
,李珩,张跃,姚天顺

软件学报 , 2001,
Abstract: In this paper, a method for automatic word sense disambiguation based on logarithm model (LM) is discussed, and a word sense disambiguation system LM_WSD is implemented. In the experiments, four models are used to word sense disambiguation. Experiments showed the effect of high-frequency sense, salient words, specialized field and general usage to noun and verb word sense disambiguation. Now the system LM_WSD was applied in a word based English-Chinese machine translation system for car fittings field, and improved the performance of the system.
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