Paper: Acquiring Sense Tagged Examples using Relevance Feedback

ACL ID C08-1102
Title Acquiring Sense Tagged Examples using Relevance Feedback
Venue International Conference on Computational Linguistics
Session Main Conference
Year 2008
Authors

Supervised approaches to Word Sense Dis- ambiguation (WSD) have been shown to outperform other approaches but are ham- pered by reliance on labeled training ex- amples (the data acquisition bottleneck). This paper presents a novel approach to the automatic acquisition of labeled examples for WSD which makes use of the Informa- tion Retrieval technique of relevance feed- back. This semi-supervised method gener- ates additional labeled examples based on existing annotated data. Our approach is applied to a set of ambiguous terms from biomedical journal articles and found to significantly improve the performance of a state-of-the-art WSD system.