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Automatic Discovery of SimilarWords

  • Pierre Senellart
  • Vincent D. Blondel
Chapter

The purpose of this chapter is to review some methods used for automatic extraction of similar words from different kinds of sources: large corpora of documents, the World Wide Web, and monolingual dictionaries. The underlying goal of these methods is in general the automatic discovery of synonyms. This goal, however, is most of the time too difficult to achieve since it is often hard to distinguish in an automatic way among synonyms, antonyms, and, more generally, words that are semantically close to each others. Most methods provide words that are “similar” to each other, with some vague notion of semantic similarity. We mainly describe two kinds of methods: techniques that, upon input of a word, automatically compile a list of good synonyms or near-synonyms, and techniques that generate a thesaurus (from some source, they build a complete lexicon of related words). They differ because in the latter case, a complete thesaurus is generated at the same time while there may not be an entry in the thesaurus for each word in the source. Nevertheless, the purposes of both sorts of techniques are very similar and we shall therefore not distinguish much between them.

Keywords

Vector Space Model Similar Word Neighborhood Graph Automatic Discovery Principal Eigenvector 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag London Limited 2008

Authors and Affiliations

  • Pierre Senellart
    • 1
  • Vincent D. Blondel
    • 2
  1. 1.INRIA Futurs & Université Paris-SudOrsay CedexFrance
  2. 2.Division of Applied MathematicsUniversité de LouvainLouvain-la-neuveBelgium

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