Paper: Example-Based Metonymy Recognition For Proper Nouns

ACL ID E06-3009
Title Example-Based Metonymy Recognition For Proper Nouns
Venue Annual Meeting of The European Chapter of The Association of Computational Linguistics
Session Main Conference
Year 2006

Metonymy recognition is generally ap- proached with complex algorithms that rely heavily on the manual annotation of training and test data. This paper will re- lieve this complexity in two ways. First, it will show that the results of the cur- rent learning algorithms can be replicated by the ‘lazy’ algorithm of Memory-Based Learning. This approach simply stores all training instances to its memory and clas- sifies a test instance by comparing it to all training examples. Second, this paper will argue that the number of labelled training examples that is currently used in the lit- erature can be reduced drastically. This finding can help relieve the knowledge ac- quisition bottleneck in metonymy recog- nition, and allow the algorithms to be ap- plied on a wider scale.