Paper: Relieving The Data Acquisition Bottleneck In Word Sense Disambiguation

ACL ID P04-1039
Title Relieving The Data Acquisition Bottleneck In Word Sense Disambiguation
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2004
Authors
  • Mona Diab (Stanford University, Stanford CA)

Supervised learning methods for WSD yield better performance than unsupervised methods. Yet the availability of clean training data for the former is still a severe challenge. In this paper, we present an unsupervised bootstrapping approach for WSD which exploits huge amounts of automatically gen- erated noisy data for training within a supervised learning framework. The method is evaluated using the 29 nouns in the English Lexical Sample task of SENSEVAL2. Our algorithm does as well as super- vised algorithms on 31% of this test set, which is an improvement of 11% (absolute) over state-of-the-art bootstrapping WSD algorithms. We identify seven different factors that impact the performance of our system.