Paper: Syntactic Features And Word Similarity For Supervised Metonymy Resolution

ACL ID P03-1008
Title Syntactic Features And Word Similarity For Supervised Metonymy Resolution
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2003
Authors

We present a supervised machine learning algorithm for metonymy resolution, which exploits the similarity between examples of conventional metonymy. We show that syntactic head-modifier relations are a high precision feature for metonymy recognition but suffer from data sparse- ness. We partially overcome this problem by integrating a thesaurus and introduc- ing simpler grammatical features, thereby preserving precision and increasing recall. Our algorithm generalises over two levels of contextual similarity. Resulting infer- ences exceed the complexity of inferences undertaken in word sense disambiguation. We also compare automatic and manual methods for syntactic feature extraction.