Sequential model selection for word sense disambiguation

Ted Pedersen, Rebecca Bruce, Janyce Wiebe

Research output: Contribution to conferencePaperpeer-review

8 Scopus citations


Statistical models of word-sense disambiguation are often based on a small number of contextual features or on a model that is assumed to characterize the interactions among a set of features. Model selection is presented as an alternative to these approaches, where a sequential search of possible models is conducted in order to find the model that best characterizes the interactions among features. This paper expands existing model selection methodology and presents the first comparative study of model selection search strategies and evaluation criteria when applied to the problem of building probabilistic classifiers for word-sense disambiguation.

Original languageEnglish (US)
Number of pages8
StatePublished - 1997
Externally publishedYes
Event5th Conference on Applied Natural Language Processing, ANLP 1997 - Washington, United States
Duration: Mar 31 1997Apr 3 1997


Conference5th Conference on Applied Natural Language Processing, ANLP 1997
Country/TerritoryUnited States

Bibliographical note

Publisher Copyright:
© 1997, Association for Computational Linguistics.


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