Learning to disambiguate by combining multiple sense representations
This talk will discuss ongoing work investigating the combination of multiple sense representation methods for word sense disambiguation (WSD). A variety of recent methods have been proposed for learning representations of semantic senses in different domains, and there is some evidence that different methods capture complementary information for WSD. We consider a simple but competitive cosine similarity-based model for WSD, and augment it by learning to produce a context-sensitive linear transformation of representations of candidate senses. In addition to transforming the input sense space, our method allows us to jointly project multiple sense representations into a single space. We find that a single learned projection matches or outperforms directly updated sense embeddings for single embedding methods, and demonstrate that combining multiple representations improves over any individual method alone. Further, by transforming and conjoining complete embedding spaces, we gain the ability to transfer model knowledge to ambiguous terms not seen during training; we are currently investigating the effectiveness of this transfer.