Paper: Sequential Model Selection For Word Sense Disambiguation

ACL ID A97-1056
Title Sequential Model Selection For Word Sense Disambiguation
Venue Applied Natural Language Processing Conference
Session Main Conference
Year 1997

Statistical models of word-sense disam- biguation are often based on a small num- ber of contextual features or on a model that is assumed to characterize the inter- actions 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 method- ology 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.