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Acquiring Sense Tagged Examples using Relevance Feedback

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If you have a question about this talk, please contact Diarmuid Ó Séaghdha.

At this session of the NLIP Reading Group we’ll be discussing the following paper:

Mark Stevenson, Yikun Guo and Robert Gaizauskas. 2008. Acquiring Sense Tagged Examples using Relevance Feedback. In Proceedings of the 22nd International Conference on Computational Linguistics (COLING-08).

Abstract: Supervised approaches to Word Sense Disambiguation (WSD) have been shown to outperform other approaches but are hampered by reliance on labeled training examples (the data acquisition bottleneck). This paper presents a novel approach to the automatic acquisition of labeled examples for WSD which makes use of the Information Retrieval technique of relevance feedback. This semi-supervised method generates 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.

This talk is part of the Natural Language Processing Reading Group series.

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