The effect of ambiguity on the automated acquisition of WSD examples

Authors:
Mark Stevenson;Yikun Guo
Affiliations:
University of Sheffield, Sheffield, United Kingdom;University of Sheffield, Sheffield, United Kingdom
Venue:
HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Year:
2010

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Abstract

Several methods for automatically generating labeled examples that can be used as training data for WSD systems have been proposed, including a semi-supervised approach based on relevance feedback (Stevenson et al., 2008a). This approach was shown to generate examples that improved the performance of a WSD system for a set of ambiguous terms from the biomedical domain. However, we find that this approach does not perform as well on other data sets. The levels of ambiguity in these data sets are analysed and we suggest this is the reason for this negative result.