Graph Connectivity Measures for Unsupervised Word Sense Disambiguation
Proceedings of the Twentieth International Joint Conference on Artificial Intelligence. Pages 1683-1687.
Abstract
Word sense disambiguation (WSD) has been a long-standing research objective for natural language processing. In this paper we are concerned with developing graph-based unsupervised algorithms for alleviating the data requirements 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 propose a variety of measures that analyze the connectivity of graph structures, thereby identifying the most relevant word senses. We assess their performance on standard datasets, and show that the best measures perform comparably to state-of-the-art.
@inproceedings{ijcai2007p272,
title = {Graph Connectivity Measures for Unsupervised Word Sense Disambiguation},
author = {Navigli, Roberto and Lapata, Mirella},
booktitle = {Proceedings of the Twentieth International Joint Conference on
Artificial Intelligence, {IJCAI-07}},
publisher = {International Joint Conferences on Artificial Intelligence},
editor = {Ramon López de Mántaras},
pages = {1683--1687},
year = {2007},
url = {https://ijcai.org/proceedings/2007/272},
}
TY - CPAPER TI - Graph Connectivity Measures for Unsupervised Word Sense Disambiguation AU - Navigli, Roberto AU - Lapata, Mirella T2 - Proceedings of the Twentieth International Joint Conference on Artificial Intelligence, IJCAI-07 PB - International Joint Conferences on Artificial Intelligence PY - 2007 SP - 1683 EP - 1687 UR - https://ijcai.org/proceedings/2007/272 ER -Copied
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