中国科学院自动化研究所机构知识库

A novel word sense disambiguation (WSD) discriminative model is proposed in this paper to handle long distance sense dependency and multi-reference lexicon dependency (i.e., the sense of a lexicon might depend on several other non-local lexicons under the same subtree) within the sentence. Many WSD systems only adopt local context to independently decide the sense of each lexicon in a sentence. However, the sense of a target word actually also depends on those structure related sense/lexicons that might be far away from it. Therefore, we propose a supervised approach which integrates structural context (for long distance sense dependency and multi-reference lexicon dependency) with the local context (for local dependency) to handle the problems mentioned above. As the result, the sense of each word is decided not only based on the local lexicons, but also based on various reference sense/lexicons (might be non-local) specified by all its associated syntactic subtrees. Experimental results show that the proposed approach significantly outperforms other state-of-art WSD systems.