LITERATURE SURVEY
 

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  • "Introduction to the special issue on word sense disambiguation: The state of the art.", Ide, N. and Véronis, J., 1998. (pdf)

           In this paper, history of WSD is presented starting from the 1950's. It covers the major areas of work and sketches the broad lines of development in this field.  

  • "Five papers onWordNet.", Miller, G., Leacock, C., Tengi, R., Bunker, R., and Miller, K.,1990. (ps) 

              In these 5 papers, information about WordNet structure and details about the relations between synsets are presented.

     

  • "Using Syntactic Dependency as Local Context to Resolve Word Sense Ambiguity", Dekang Lin, 1997. (pdf)

                The intuition "Two different words are likely to have similar meanings if they occur in identical local contexts" is adopted in this paper. Disambiguation is done based on syntactic dependency and sense similarity.

     

  • "Automatic Retrieval and Clustering of Similar Words", Dekang Lin, 1998. (pdf)

                 A new methodology for constructing a thesaurus using parsed corpus is presented in this paper.

     

  • "Discovering word senses from text", Patrick Pantel and Dekang Lin, 2002. (pdf)

             A clustering algorithm called CBC (Clustering By Committee) that automatically discovers word senses from text is presented in this paper. Each cluster that a word belongs to represents one of its senses. An evaluation methodology for automatically measuring the precision and recall of discovered senses is also presented.

  • "A simple approach to building ensembles of naive bayesian classifiers for word sense disambiguation." , Ted Pedersen, 2000. (pdf)

           This paper presents a corpus-based approach to word sense disambiguation that builds an ensemble of Naive Bayesian classifiers. Each Bayesian classifier is based on lexical features that represent co-occurring words in varying sized windows of context.

  • "Using measures of semantic relatedness for word sense disambiguation", S. Patwardhan, S. Banerjee and T. Pedersen, 2003. (pdf)

            This paper generalizes an adapted Lesk algorithm to a method of WSD based on semantic relatedness.

  • "Finding predominant word senses in untagged text", D. McCarthy et al., 2004. (pdf)

            The work presented in this paper depends on the use of a thesaurus acquired from raw textual corpora and the WordNet similarity package to find predominant noun senses automatically.

  • "A method for disambiguating word senses in a large corpus.", B. Gale, K. Church, and D. Yarowsky, 1992, Computers and the Humanities, 26:415-439.

           In this paper a variant of Bayes ratio on six ambiguous nouns is presented and the paper reports 92% accuracy.

  • "One sense per discourse."  B. Gale, K. Church, and D. Yarowsky, 1992. (pdf)

           Based on the observation that it is extremely unusual to find two or more senses of a polysemous word in the same discourse, "one sense per discourse" heuristic is proposed in this paper.

  •  "Unsupervised word sense disambiguation rivaling supervised methods",Yarowsky, D., 1995.  (pdf)

            This paper presents an unsupervised learning algorithm for sense disambiguation. The algorithm is based on two powerful constraints -one sense per discourse and one sense per collocation- exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96%.

  • "Hierarchical decision lists for word sense disambiguation", D. Yarowsky, 2000. (pdf)

          This paper describes a supervised algorithm for word sense disambiguation based on hierarchies of decision lists. This algorithm supports a useful degree of conditional branching while minimizing the training data fragmentation typical of decision trees.

  • "Evaluating sense disambiguation across diverse parameter spaces", D. Yarowsky and R. Florian, 2002. (pdf)

This article presents a comprehensive empirical exploration and evaluation of a diverse range of data characteristics which infuence word sense disambiguation performance. It focuses on a set of 6 core supervised algorithms, including 3 variants of Bayesian classifiers, a cosine model, non-hierarchical decision lists, and an extension of the transformation-based learning model.

  • "Comparative experiments on disambiguating word senses: An illustration of the role of   bias in machine learning" R. Mooney, 1996. (pdf)

           This paper describes an experimental comparison of seven different learning algorithms on the problem of learning to disambiguate the meaning of a word from context. The algorithms tested include statistical, neural-network, decision-tree, rule-based, and case-based classification techniques.

  • "Combining classifiers for word sense disambiguation", R. Florian,S. Cucerzan, C. Schafer,  and D. Yarowsky, 2002. (pdf)

          R. Florian combined four classifiers namely feature-enhanced Naïve Bayes, Cosine, bag-of-words Naïve Bayes and non-hierarchical decision lists, and obtained good results which were shown in this paper.

  • "Exploring automatic word sense disambiguation with decision lists and the web", E. Agirre, and D.Martinez, 2000. (pdf)

          Supervised learning seems to be stuck because of the knowledge acquisition bottleneck. In this paper, an in-depth study of the performance of decision lists on two publicly available corpora and an additional corpus automatically acquired from the Web is presented.

  • "Integrating multiple knowledge sources to disambiguate word sense: An exemplar-based approach", Ng, H. T. and Lee, H. B., 1996. (pdf)

           In this paper, a new approach for word sense disambiguation using an exemplar-based learning algorithm is presented. This approach integrates a diverse set of knowledge sources to disambiguate word sense, including part of speech of neighboring words, morphological form, the unordered set of surrounding words, local collocations, and verb-object syntactic relation.

  • "Word sense disambiguation using statistical methods", Brown, Peter F., Stephen, D.P., Vincent, J.D.P., and Robert, L.M., 1991. (pdf)

           A statistical technique for assigning senses to words is presented in this paper. An instance of a word is assigned a sense by asking a question about the context in which the word appears.

  • "Noun homograph disambiguation using local context in large text corpora", Hearst, M., 1991. (pdf)

           The algorithm presented in this paper checks the context surrounding the target noun against that of previously observed instances and chooses the sense for which the most evidence is found, where evidence consists of a set of orthographic, syntactic, and lexical features.

  • "An automatic method for generating sense tagged corpora", R. Mihalcea and Dan I. Moldovan, 1999. (pdf)

          This paper presents a novel approach to automatically generate arbitrarily large corpora for word senses. The method is based on the information provided in WordNet and the information gathered from the Internet using existing search engines.

  • "An iterative approach to word sense disambiguation", R. Mihalcea and Dan I. Moldovan, 2000. (pdf)

           In this paper an iterative algorithm which combines WordNet and a semantic tagged corpus is presented.

  • "A highly accurate bootstrapping algorithm for word sense disambiguation", Mihalcea, R. and Moldovan, D., 2001. (pdf)

          A bootstrapping algorithm for Word Sense Disambiguation which uses WordNet and a semantic tagged corpus is presented in this paper.

  • "Bootstrapping Large Sense Tagged Corpora", Mihalcea, R., 2002. (pdf)

         This paper proposes a generation algorithm that may be used to automatically create large sense tagged corpora.

  • "Similarity-based word sense disambiguation", Y. Karov and S. Edelman, 1998. (pdf)

          The method presented in this paper is based on word similarity and context similarity measures. Words are considered similar if they appear in similar contexts; contexts are similar if they contain similar words.

  • "Accurate Methods for the Statistics of Surprise and Coincidence", Ted Dunning, 1993. (pdf)

In this paper the likelihood estimation used in the thesis is explained.