Paper: Knowledge-Rich Word Sense Disambiguation Rivaling Supervised Systems

ACL ID P10-1154
Title Knowledge-Rich Word Sense Disambiguation Rivaling Supervised Systems
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2010
Authors

One of the main obstacles to high- performance Word Sense Disambigua- tion (WSD) is the knowledge acquisi- tion bottleneck. In this paper, we present a methodology to automatically extend WordNet with large amounts of seman- tic relations from an encyclopedic re- source, namely Wikipedia. We show that, when provided with a vast amount of high-quality semantic relations, sim- ple knowledge-lean disambiguation algo- rithms compete with state-of-the-art su- pervisedWSDsystemsinacoarse-grained all-words setting and outperform them on gold-standard domain-specific datasets.