Using Curvature and Markov Clustering in Graphs for Lexical Acquisition and Word Sense Discrimination Export

@article{citeulike:4156974, abstract = {We introduce two different approaches for clustering semantically similarwords. We accommodate ambiguity by allowing a word to belong to severalclusters.Both methods use a graph-theoretic representation of words and theirparadigmatic relationships. The first approach is based on the concept ofcurvature and divides the word graph into classes of similar words by removingwords of low curvature which connect several dispersed clusters.The second method, instead of clustering the nodes, clusters the links in ourgraph. These contain more specific contextual information than nodesrepresenting just words. In so doing, we naturally accommodate ambiguity byallowing multiple class membership.Both methods are evaluated on a lexical acquisition task, using clustering toadd nouns to the WordNet taxonomy. The most effective method is linkclustering.}, address = {Trento}, archivePrefix = {arXiv}, author = {Dorow, Beate and Widdows, Dominic and Ling, Katarina and Eckmann, Jean-Pierre and Sergi, Danilo and Moses, Elisha}, booktitle = {2nd MEANING Workshop}, citeulike-article-id = {4156974}, citeulike-linkout-0 = {http://arxiv.org/abs/cond-mat/0403693}, citeulike-linkout-1 = {http://arxiv.org/pdf/cond-mat/0403693}, citeulike-linkout-2 = {http://www.puttypeg.com/papers/curvature-links.pdf}, day = {3-4}, eprint = {cond-mat/0403693}, keywords = {clustering, entityguides, graph}, month = {February}, posted-at = {2009-03-10 07:24:50}, priority = {0}, title = {Using Curvature and Markov Clustering in Graphs for Lexical Acquisition and Word Sense Discrimination}, url = {http://arxiv.org/abs/cond-mat/0403693}, year = {2005} }

TY - JOUR ID - citeulike:4156974 L3 - citeulike-article-id:4156974 N2 - We introduce two different approaches for clustering semantically similarwords. We accommodate ambiguity by allowing a word to belong to severalclusters.Both methods use a graph-theoretic representation of words and theirparadigmatic relationships. The first approach is based on the concept ofcurvature and divides the word graph into classes of similar words by removingwords of low curvature which connect several dispersed clusters.The second method, instead of clustering the nodes, clusters the links in ourgraph. These contain more specific contextual information than nodesrepresenting just words. In so doing, we naturally accommodate ambiguity byallowing multiple class membership.Both methods are evaluated on a lexical acquisition task, using clustering toadd nouns to the WordNet taxonomy. The most effective method is linkclustering. AD - Trento TI - Using Curvature and Markov Clustering in Graphs for Lexical Acquisition and Word Sense Discrimination T2 - 2nd MEANING Workshop KW - clustering KW - entityguides KW - graph AU - Dorow, Beate AU - Widdows, Dominic AU - Ling, Katarina AU - Eckmann, Jean-Pierre AU - Sergi, Danilo AU - Moses, Elisha PY - 2005/02/3-4/ UR - http://arxiv.org/abs/cond-mat/0403693 ER -