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.

An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation / Navigli, Roberto; M., Lapata. - In: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. - ISSN 0162-8828. - STAMPA. - 32:4(2010), pp. 678-692. [10.1109/tpami.2009.36]

An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation

NAVIGLI, ROBERTO;
2010

Abstract

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.
2010
graph connectivity; semantic networks; social network analysis; social network analysis.; word sense disambiguation
01 Pubblicazione su rivista::01a Articolo in rivista
An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation / Navigli, Roberto; M., Lapata. - In: IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE. - ISSN 0162-8828. - STAMPA. - 32:4(2010), pp. 678-692. [10.1109/tpami.2009.36]
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/143403
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