Paper: Combining Lexical And Syntactic Features For Supervised Word Sense Disambiguation

ACL ID W04-2404
Title Combining Lexical And Syntactic Features For Supervised Word Sense Disambiguation
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2004
Authors

The success of supervised learning approaches to word sense disambiguation is largely de- pendent on the features used to represent the context in which an ambiguous word occurs. Previous work has reached mixed conclusions; some suggest that combinations of syntactic and lexical features will perform most effec- tively. However, others have shown that sim- ple lexical features perform well on their own. This paper evaluates the effect of using differ- ent lexical and syntactic features both individu- ally and in combination. We show that it is pos- sible for a very simple ensemble that utilizes a single lexical feature and a sequence of part of speech features to result in disambiguation ac- curacy that is near state of the art.