Paper: Semi-Supervised WSD in Selectional Preferences with Semantic Redundancy

ACL ID C10-2142
Title Semi-Supervised WSD in Selectional Preferences with Semantic Redundancy
Venue International Conference on Computational Linguistics
Session Poster Session
Year 2010
Authors

This paper proposes a semi-supervised approach for WSD in Word-Class based selectional preferences. The approach exploits syntagmatic and paradigmatic semantic redundancy in the semantic system and uses association computation and minimum description length for the task of WSD. Experiments on Predicate-Object collocations and Subject-Predicate collocations with polysemous predicates in Chinese show that the proposed approach achieves a precision which is 8% higher than the semantic- association based baseline. The semi- supervised nature of the approach makes it promising for constructing large scale selectional preference knowledge base.