Paper: Applying Alternating Structure Optimization To Word Sense Disambiguation

ACL ID W06-2911
Title Applying Alternating Structure Optimization To Word Sense Disambiguation
Venue International Conference on Computational Natural Language Learning
Session Main Conference
Year 2006
Authors

This paper presents a new application of the recently proposed machine learning method Alternating Structure Optimiza- tion (ASO), to word sense disambiguation (WSD). Given a set of WSD problems and their respective labeled examples, we seek to improve overall performance on that set by using all the labeled exam- ples (irrespective of target words) for the entire set in learning a disambiguator for each individual problem. Thus, in effect, on each individual problem (e.g. , disam- biguation of “art”) we benefit from train- ing examples for other problems (e.g. , disambiguation of “bar”, “canal”, and so forth). We empirically study the effective use of ASO for this purpose in the multi- task and semi-supervised learning config- urations. Our performance results rival or exceed ...