Article

Free access

Unsupervised word sense disambiguation rivaling supervised methods

Author:

David YarowskyAuthors Info & Claims

ACL '95: Proceedings of the 33rd annual meeting on Association for Computational Linguistics

June 1995

Pages 189 - 196

https://doi.org/10.3115/981658.981684

Published: 26 June 1995 Publication History

Abstract

This paper presents an unsupervised learning algorithm for sense disambiguation that, when trained on unannotated English text, rivals the performance of supervised techniques that require time-consuming hand annotations. The algorithm is based on two powerful constraints---that words tend to have one sense per discourse and one sense per collocation---exploited in an iterative bootstrapping procedure. Tested accuracy exceeds 96%.

References

[1]

Baum, L. E., "An Inequality and Associated Maximization Technique in Statistical Estimation of Probabilistic Functions of a Markov Process," Inequalities, v 3, pp 1--8, 1972.

[2]

Black, Ezra, "An Experiment in Computational Discrimination of English Word Senses," in IBM Journal of Research and Development, v 232, pp 185--194, 1988.

Digital Library

[3]

Brill, Eric, "A Corpus-Based Approach to Language Learning," Ph.D. Thesis, University of Pennsylvania, 1993.

Digital Library

[4]

Brown, Peter, Stephen Della Pietra, Vincent Della Pietra, and Robert Mercer, "Word Sense Disambiguation using Statistical Methods," Proceedings of the 29th Annual Meeting of the Association for Computational Linguistics, pp 264--270, 1991.

Digital Library

[5]

Bruce, Rebecca and Janyce Wiebe, "Word-Sense Disambiguation Using Decomposable Models," in Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, NM, 1994.

Digital Library

[6]

Church, K. W., "A Stochastic Parts Program an Noun Phrase Parser for Unrestricted Text," in Proceeding, IEEE International Conference on Acoustics, Speech and Signal Processing, Glasgow, 1989.

[7]

Dagan, Ido and Alon Itai, "Word Sense Disambiguation Using a Second Language Monolingual Corpus", Computational Linguistics, v 20, pp 563--596, 1994.

Digital Library

[8]

Dempster, A. P., Laird, N. M., and Rubin, D. B., "Maximum Likelihood From Incomplete Data via the EM Algorithm," Journal of the Royal Statistical Society, v 39, pp 1--38, 1977.

[9]

Gale, W., K. Church, and D. Yarowsky, "A Method for Disambiguating Word Senses in a Large Corpus," Computers and the Humanities, 26, pp 415--439, 1992.

[10]

Gale, W., K. Church, and D. Yarowsky. "Discrimination Decisions for 100,000-Dimensional Spaces." In A. Zampoli, N. Calzolari and M. Palmer (eds.), Current Issues in Computational Linguistics: In Honour of Don Walker, Kluwer Academic Publishers, pp. 429--450, 1994.

[11]

Guthrie, J., L. Guthrie, Y. Wilks and H. Aidinejad, "Subject Dependent Co-occurrence and Word Sense Disambiguation," in Proceedings of the 29th Annual Meeting of the Association for Computational Linguistics, pp 146--152, 1991.

Digital Library

[12]

Hearst, Marti, "Noun Homograph Disambiguation Using Local Context in Large Text Corpora," in Using Corpora, University of Waterloo, Ontario, 1991.

[13]

Leacock, Claudia, Geoffrey Towell and Ellen Voorhees "Corpus-Based Statistical Sense Resolution," in Proceedings, ARPA Human Language Technology Workshop, 1993.

Digital Library

[14]

Lehman, Jill Fain, "Toward the Essential Nature of Statistical Knowledge in Sense Resolution", in Proceedings of the Twelfth National Conference on Artificial Intelligence, pp 734--471, 1994.

Digital Library

[15]

Lesk, Michael, "Automatic Sense Disambiguation: How to tell a Pine Cone from an Ice Cream Cone," Proceeding of the 1986 SIGDOC Conference, Association for Computing Machinery, New York, 1986.

Digital Library

[16]

Miller, George, "WordNet: An On-Line Lexical Database," International Journal of Lexicography, 3, 4, 1990.

[17]

Mosteller, Frederick, and David Wallace, Inference and Disputed Authorship: The Federalist, Addison-Wesley, Reading, Massachusetts, 1964.

[18]

Rivest, R. L., "Learning Decision Lists," in Machine Learning, 2, pp 229--246, 1987.

Digital Library

[19]

Schütze, Hinrich, "Dimensions of Meaning," in Proceedings of Supercomputing '92, 1992.

Digital Library

[20]

Slator, Brian, "Using Context for Sense Preference," in Text-Based Intelligent Systems: Current Research in Text Analysis, Information Extraction and Retrieval, P. S. Jacobs, ed., GE Research and Development Center, Schenectady, New York, 1990.

[21]

Veronis, Jean and Nancy Ide, "Word Sense Disambiguation with Very Large Neural Networks Extracted from Machine Readable Dictionaries," in Proceedings, COLING-90, pp 389--394, 1990.

Digital Library

[22]

Yarowsky, David "Word-Sense Disambiguation Using Statistical Models of Roget's Categories Trained on Large Corpora," in Proceedings, COLING-92, Nantes, France, 1992.

Digital Library

[23]

Yarowsky, David, "One Sense Per Collocation," in Proceedings, ARPA Human Language Technology Workshop, Princeton, 1993.

Digital Library

[24]

Yarowsky, David, "Decision Lists for Lexical Ambiguity Resolution: Application to Accent Restoration in Spanish and French," in Proceedings of the 32nd Annual Meeting of the Association for Computational Linguistics, Las Cruces, NM, 1994.

Digital Library

[25]

Yarowsky, David. "Homograph Disambiguation in Speech Synthesis." In J. Hirschberg, R. Sproat and J. van Santen (eds.), Progress in Speech Synthesis, Springer-Verlag, to appear.

Cited By

Li ZZhao SChen CChen Q(2024)Reducing the Impact of Time Evolution on Source Code Authorship Attribution via Domain AdaptationACM Transactions on Software Engineering and Methodology10.1145/365215133:6(1-27)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3652151
Schick TDwivedi-Yu JDessí RRaileanu RLomeli MHambro EZettlemoyer LCancedda NScialom TOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)ToolformerProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669119(68539-68551)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669119
Shukla VZeng ZAhmed KVan den Broeck GOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)A unified approach to count-based weakly-supervised learningProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667802(38709-38722)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667802
Show More Cited By

Unsupervised word sense disambiguation rivaling supervised methods
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

Recommendations

An unsupervised method for word sense disambiguation
Abstract
Word sense disambiguation (WSD) finds the actual meaning of a word according to its context. This paper presents a novel WSD method to find the correct sense of a word present in a sentence. The proposed method uses both the WordNet ...
Read More
Unsupervised Word-Sense Disambiguation Using Bilingual Comparable Corpora

An unsupervised method for word-sense disambiguation using bilingual comparable corpora was developed. First, it extracts word associations, i.e., statistically significant pairs of associated words, from the corpus of each language. Then, it aligns ...
Read More
Unsupervised word sense disambiguation using bilingual comparable corpora
COLING '02: Proceedings of the 19th international conference on Computational linguistics - Volume 1

An unsupervised method for word sense disambiguation using a bilingual comparable corpus was developed. First, it extracts statistically significant pairs of related words from the corpus of each language. Then, aligning pairs of related words ...
Read More

Comments

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

ACL '95: Proceedings of the 33rd annual meeting on Association for Computational Linguistics

June 1995

354 pages

Program Chair:
Hans Uszkoreit
Saarbrücken, Germany

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 26 June 1995

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 85 of 443 submissions, 19%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

482
Total Citations
View Citations
7,981
Total Downloads

Downloads (Last 12 months)504
Downloads (Last 6 weeks)61

Other Metrics

View Author Metrics

Citations

Cited By

Li ZZhao SChen CChen Q(2024)Reducing the Impact of Time Evolution on Source Code Authorship Attribution via Domain AdaptationACM Transactions on Software Engineering and Methodology10.1145/365215133:6(1-27)Online publication date: 27-Jun-2024
https://dl.acm.org/doi/10.1145/3652151
Schick TDwivedi-Yu JDessí RRaileanu RLomeli MHambro EZettlemoyer LCancedda NScialom TOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)ToolformerProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3669119(68539-68551)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3669119
Shukla VZeng ZAhmed KVan den Broeck GOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)A unified approach to count-based weakly-supervised learningProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667802(38709-38722)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667802
Tifrea AYüce GSanyal AYang FOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Can semi-supervised learning use all the data effectively? a lower bound perspectiveProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3667085(21960-21982)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3667085
Shi ZLipani AOh ANaumann TGloberson ASaenko KHardt MLevine S(2023)Don't stop pretraining? make prompt-based fine-tuning powerful learnerProceedings of the 37th International Conference on Neural Information Processing Systems10.5555/3666122.3666378(5827-5849)Online publication date: 10-Dec-2023
https://dl.acm.org/doi/10.5555/3666122.3666378
Jia LGuo LZhou ZShao JXiang YLi YKrause ABrunskill ECho KEngelhardt BSabato SScarlett J(2023)Bidirectional adaptation for robust semi-supervised learning with inconsistent data distributionsProceedings of the 40th International Conference on Machine Learning10.5555/3618408.3619015(14886-14901)Online publication date: 23-Jul-2023
https://dl.acm.org/doi/10.5555/3618408.3619015
Feng YYi XLakshmanan LXie XElkind E(2023)KESTProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/561(5049-5057)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/561
Huang YSun ZLi GTian XDai WHu WJiang YZhou ZElkind E(2023)Enabling abductive learning to exploit knowledge graphProceedings of the Thirty-Second International Joint Conference on Artificial Intelligence10.24963/ijcai.2023/427(3839-3847)Online publication date: 19-Aug-2023
https://dl.acm.org/doi/10.24963/ijcai.2023/427
Lan YZhang YQu YWang CLi CCai JXie YWu ZWilliams BChen YNeville J(2023)Weakly supervised 3D segmentation via receptive-driven pseudo label consistency and structural consistencyProceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence and Thirty-Fifth Conference on Innovative Applications of Artificial Intelligence and Thirteenth Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v37i1.25205(1222-1230)Online publication date: 7-Feb-2023
https://dl.acm.org/doi/10.1609/aaai.v37i1.25205
Eren MBhattarai MJoyce RRaff ENicholas CAlexandrov B(2023)Semi-Supervised Classification of Malware Families Under Extreme Class Imbalance via Hierarchical Non-Negative Matrix Factorization with Automatic Model SelectionACM Transactions on Privacy and Security10.1145/362456726:4(1-27)Online publication date: 13-Nov-2023
https://dl.acm.org/doi/10.1145/3624567
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

eReader

View online with eReader.

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Table of Contents