Paper: All Words Domain Adapted WSD: Finding a Middle Ground between Supervision and Unsupervision

ACL ID P10-1155
Title All Words Domain Adapted WSD: Finding a Middle Ground between Supervision and Unsupervision
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

In spite of decades of research on word sense disambiguation (WSD), all-words general purpose WSD has remained a dis- tant goal. Many supervised WSD systems have been built, but the effort of creat- ing the training corpus - annotated sense marked corpora - has always been a matter of concern. Therefore, attempts have been made to develop unsupervised and knowl- edge based techniques for WSD which do not need sense marked corpora. However such approaches have not proved effective, since they typically do not better Word- net first sense baseline accuracy. Our re- search reported here proposes to stick to the supervised approach, but with far less demand on annotation. We show that if we have ANY sense marked corpora, be it from mixed domain or a specific domain, a small amount of annotatio...