Related Paper - Jan 27

I read the paper :

E. Shutova, L. Sun and A. Korhonen. 2010. Metaphor Identification Using Verb and Noun Clustering. In Proceedings of COLING 2010, Beijing, China.



http://www.cl.cam.ac.uk/~es407/papers/Coling10.pdf

The paper describes a word clustering approach to metaphor identification. Their decision to use word clustering is based on hypothesis that target concepts associated with a source concept appear in similar lexico-syntactic environments, and clustering will capture this relatedness by association. The method starts with a small set of seeds of source-target domain mappings, extracts rich features from a shallow parser, and uses spectral method to perform noun and verb clustering. The resulting noun clusters are considered as target concepts in the same source domain, and the resulting verb clusters are considered as source domain lexicon.

As for the results, they were able to get some nice metaphors that represent broad semantic classes such as {swallow anger, hurl comment, spark enthusiasm, etc.} from seeds {stir excitement, throw remark, cast doubt}, which the WordNet-based approach (baseline) cannot acquire. They evaluated the methods using precision, and got 0.79 (baseline 0.44). I don't think these numbers are convincing though since they randomly sampled sentences annotated by the systems and asked five human annotators to judge, but they did not report the size of the sample (or maybe I missed it?). Also, though there is no large annotated corpus for metaphor identification, it would be nice if they had reported recall on smaller data just to get an idea of the coverage of the method.