Journal
IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE
Volume 32, Issue 4, Pages 678-692Publisher
IEEE COMPUTER SOC
DOI: 10.1109/TPAMI.2009.36
Keywords
Word sense disambiguation; graph connectivity; semantic networks; social network analysis
Funding
- EPSRC [EP/C538447/1]
- Engineering and Physical Sciences Research Council [EP/C538447/1] Funding Source: researchfish
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Word sense disambiguation (WSD), the task of identifying the intended meanings (senses) of words in context, has been a long-standing research objective for natural language processing. In this paper, we are concerned with graph-based algorithms for large-scale WSD. Under this framework, finding the right sense for a given word amounts to identifying the most important node among the set of graph nodes representing its senses. We introduce a graph-based WSD algorithm which has few parameters and does not require sense-annotated data for training. Using this algorithm, we investigate several measures of graph connectivity with the aim of identifying those best suited for WSD. We also examine how the chosen lexicon and its connectivity influences WSD performance. We report results on standard data sets and show that our graph-based approach performs comparably to the state of the art.
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