%0 Journal Article %1 navigli2010experimental %A Navigli, Roberto %A Lapata, Mirella %C Washington, DC, USA %D 2010 %I IEEE Computer Society %J IEEE Trans. Pattern Anal. Mach. Intell. %K disambiguation graph sense unsupervised word %N 4 %P 678--692 %R 10.1109/TPAMI.2009.36 %T An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation %U http://dx.doi.org/10.1109/TPAMI.2009.36 %V 32 %X 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.

@article{navigli2010experimental, 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.}, acmid = {1731191}, added-at = {2017-07-17T16:34:19.000+0200}, address = {Washington, DC, USA}, author = {Navigli, Roberto and Lapata, Mirella}, biburl = {https://www.bibsonomy.org/bibtex/272e49b7320f165c95304cf6f318a7c45/thoni}, description = {An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation}, doi = {10.1109/TPAMI.2009.36}, interhash = {5c5fc6ffa35fb345b93c6322c99c6ad3}, intrahash = {72e49b7320f165c95304cf6f318a7c45}, issn = {0162-8828}, issue_date = {April 2010}, journal = {IEEE Trans. Pattern Anal. Mach. Intell.}, keywords = {disambiguation graph sense unsupervised word}, month = apr, number = 4, numpages = {15}, pages = {678--692}, publisher = {IEEE Computer Society}, timestamp = {2017-07-17T16:34:19.000+0200}, title = {An Experimental Study of Graph Connectivity for Unsupervised Word Sense Disambiguation}, url = {http://dx.doi.org/10.1109/TPAMI.2009.36}, volume = 32, year = 2010 }