Paper: A Clustering Approach For Nearly Unsupervised Recognition Of Nonliteral Language

ACL ID E06-1042
Title A Clustering Approach For Nearly Unsupervised Recognition Of Nonliteral Language
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2006
Authors

In this paper we present TroFi (Trope Finder), a system for automatically classi- fying literal and nonliteral usages of verbs through nearly unsupervised word-sense disambiguation and clustering techniques. TroFi uses sentential context instead of selectional constraint violations or paths in semantic hierarchies. It also uses lit- eral and nonliteral seed sets acquired and cleaned without human supervision in or- der to bootstrap learning. We adapt a word-sense disambiguation algorithm to our task and augment it with multiple seed set learners, a voting schema, and addi- tional features like SuperTags and extra- sentential context. Detailed experiments on hand-annotated data show that our en- hanced algorithm outperforms the base- line by 24.4%. Using the TroFi algo- rithm, we also build...