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 language||English (US)|
|Number of pages||8|
|State||Published - 1997|
|Event||5th Conference on Applied Natural Language Processing, ANLP 1997 - Washington, United States|
Duration: Mar 31 1997 → Apr 3 1997
|Conference||5th Conference on Applied Natural Language Processing, ANLP 1997|
|Period||3/31/97 → 4/3/97|
Bibliographical noteFunding Information:
Suppose our training sample has N sense-tagged sentences. There are q possible combinations of values for the n feature variables, where each such combination is represented by a feature vector. Let • This research was supported by the Office of Naval Research under grant number N00014-95-1-0776.
© 1997, Association for Computational Linguistics.