Word Sense Disambiguation - A Context based Multimode WSD Model

For many decades researchers in the domain of NLP (Natural Language Processing) and its applications like Machine Translation, Text Mining, Question Answering, Information Extraction and Information retrieval etc. have been posed with a challenging area of research i.e. WSD (Word Sense Disambiguation) WSD can be defined as the ability to correctly ascertain the meaning of a word, with reference to the context in which the word was used. Linguistics, has defined context as the passage, sentence or text in which the word appears that is used for ascertaining its meaning. Thus, context is dependent on the POS (Part Of Speech) where the word is used e.g. Adverb, Adjective, Pronoun, Verb and Noun. In the following study we recommend a unique way of WSD based on Context through WordNet, multimodal algorithm that is knowledge based, map-reduce and soft sense WSD.

[1]  Philip Resnik,et al.  Using Information Content to Evaluate Semantic Similarity in a Taxonomy , 1995, IJCAI.

[2]  Graeme Hirst,et al.  Semantic distance in WordNet: An experimental, application-oriented evaluation of five measures , 2004 .

[3]  Christiane Fellbaum,et al.  Combining Local Context and Wordnet Similarity for Word Sense Identification , 1998 .

[4]  Pasquale Lops,et al.  Combining Learning and Word Sense Disambiguation for Intelligent User Profiling , 2007, IJCAI.

[5]  Michael E. Lesk,et al.  Automatic sense disambiguation using machine readable dictionaries: how to tell a pine cone from an ice cream cone , 1986, SIGDOC '86.

[6]  Vedat Coskun,et al.  A new semantic similarity measure evaluated in word sense disambiguation , 2005, NODALIDA.

[7]  Dekang Lin,et al.  An Information-Theoretic Definition of Similarity , 1998, ICML.

[8]  Martha Palmer,et al.  Verb Semantics and Lexical Selection , 1994, ACL.

[9]  Roberto Navigli,et al.  Word sense disambiguation: A survey , 2009, CSUR.

[10]  David W. Conrath,et al.  Semantic Similarity Based on Corpus Statistics and Lexical Taxonomy , 1997, ROCLING/IJCLCLP.

[11]  Martin Chodorow,et al.  Combining local context and wordnet similarity for word sense identification , 1998 .

[12]  Rada Mihalcea,et al.  Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity , 2007, International Conference on Semantic Computing (ICSC 2007).

[13]  A. Babu,et al.  Word Sense Disambiguation : An Empirical Survey , 2012 .

[14]  Eneko Agirre,et al.  Word Sense Disambiguation: Algorithms and Applications , 2007 .

[15]  Ted Pedersen,et al.  An Adapted Lesk Algorithm for Word Sense Disambiguation Using WordNet , 2002, CICLing.

[16]  Rada Mihalcea,et al.  Unsupervised Graph-basedWord Sense Disambiguation Using Measures of Word Semantic Similarity , 2007 .

[17]  Alexander Gelbukh,et al.  Comparing Similarity Measures for Original WSD Lesk Algorithm , 2009 .

[18]  Win Thandar Aung,et al.  Word Sense Disambiguation: A Briefly Survey , 2013 .