Cogprints
Login | Create Account

Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities

Turney, Peter D. (2004) Word Sense Disambiguation by Web Mining for Word Co-occurrence Probabilities. [Conference Paper]

[img]
Preview
PDF
36Kb

Abstract

This paper describes the National Research Council (NRC) Word Sense Disambiguation (WSD) system, as applied to the English Lexical Sample (ELS) task in Senseval-3. The NRC system approaches WSD as a classical supervised machine learning problem, using familiar tools such as the Weka machine learning software and Brill's rule-based part-of-speech tagger. Head words are represented as feature vectors with several hundred features. Approximately half of the features are syntactic and the other half are semantic. The main novelty in the system is the method for generating the semantic features, based on word co-occurrence probabilities. The probabilities are estimated using the Waterloo MultiText System with a corpus of about one terabyte of unlabeled text, collected by a web crawler.

Item Type:Conference Paper
Subjects:Computer Science > Language
Linguistics > Computational Linguistics
Linguistics > Semantics
Computer Science > Machine Learning
ID Code:3732
Deposited By:Turney, Peter
Deposited On:30 Jul 2004
Last Modified:19 Dec 2009 19:20

References in Article

Select the SEEK icon to attempt to find the referenced article. If it does not appear to be in cogprints you will be forwarded to the paracite service. Poorly formated references will probably not work.

Eric Brill. 1994. Some advances in transformation-based part of speech tagging. In Proceedings of the 12th National Conference on Artificial Intelligence (AAAI-94), pages 722-727.

Charles L.A. Clarke and Gordon V. Cormack. 2000. Shortest substring retrieval and ranking. ACM Transactions on Information Systems (TOIS), 18(1):44-78.

Charles L.A. Clarke, G.V. Cormack, and F.J. Burkowski. 1995. An algebra for structured text search and a framework for its implementation. The Computer Journal, 38(1):43-56.

Egidio L. Terra and Charles L.A. Clarke. 2003. Frequency estimates for statistical word similarity measures. In Proceedings of the Human Language Technology and North American Chapter of Association of Computational Linguistics Conference 2003 (HLT/NAACL 2003), pages 244-251.

Peter D. Turney. 2001. Mining the Web for synonyms: PMI-IR versus LSA on TOEFL. In Proceedings of the Twelfth European Conference on Machine Learning (ECML-2001), pages 491-502.

Peter D. Turney. 2003. Coherent keyphrase extraction via Web mining. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI-03), pages 434-439.

Ian H. Witten and Eibe Frank. 1999. Data Mining: Practical Machine Learning Tools and Techniques with Java Implementations. Morgan Kaufmann, San Mateo, CA.

D. Yarowsky, S. Cucerzan, R. Florian, C. Schafer, and R. Wicentowski. 2001. The Johns Hopkins SENSEVAL2 system descriptions. In Proceedings of SENSEVAL2, pages 163-166.

Metadata

Repository Staff Only: item control page

Cogprints is powered by EPrints 3 which is developed by the School of Electronics and Computer Science at the University of Southampton. More information and software credits.