\u677e\u7530\u3055\u3093\u306e\u8f2a\u8b1b\u3067\u3059\u3002<\/p>\n
Ioannis P. Klapaftis and Suresh Manandhar
\nWord Sense Induction & Disambiguation Using Hierarchical Random Graphs
\n(EMNLP 2010)
\nhttp:\/\/www.aclweb.org\/anthology\/D\/D10\/D10-1073.pdf<\/p>\n
Abstract:<\/p>\n
Graph-based methods have gained attention in many areas of Natural Language Processing (NLP) including Word Sense Disambiguation (WSD), text summarization, keyword extraction and others.<\/p>\n
Most of the work in these areas formulate their problem in a graph-based setting and apply unsupervised graph clustering to obtain a set of clusters.<\/p>\n
Recent studies suggest that graphs often exhibit a hierarchical structure that goes beyond simple flat clustering.<\/p>\n
This paper presents an unsupervised method for inferring the hierarchical grouping of the senses of a polysemous word.<\/p>\n
The inferred hierarchical structures are applied to the problem of word sense disambiguation, where we show that our method performs significantly better than traditional graph-based methods and agglomerative clustering yielding improvements over state-of-the-art WSD systems based on sense induction.<\/p>\n
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