Article

Free access

A report of recent progress in transformation-based error-driven learning

Author:

Eric BrillAuthors Info & Claims

HLT '94: Proceedings of the workshop on Human Language Technology

Pages 256 - 261

https://doi.org/10.3115/1075812.1075869

Published: 08 March 1994 Publication History

Abstract

Most recent research in trainable part of speech taggers has explored stochastic tagging. While these taggers obtain high accuracy, linguistic information is captured indirectly, typically in tens of thousands of lexical and contextual probabilities. In [Brill 92], a trainable rule-based tagger was described that obtained performance comparable to that of stochastic taggers, but captured relevant linguistic information in a small number of simple non-stochastic rules. In this paper, we describe a number of extensions to this rule-based tagger. First, we describe a method for expressing lexical relations in tagging that stochastic taggers are currently unable to express. Next, we show a rule-based approach to tagging unknown words. Finally, we show how the tagger can be extended into a k-best tagger, where multiple tags can be assigned to words in some cases of uncertainty.

References

[1]

{Brill 92} E. Brill 1992. A simple rule-based part of speech tagger. In Proceedings of the Third Conference on Applied Natural Language Processing, Trento, Italy.]]

Digital Library

[2]

{Brill 93} E. Brill 1993. Automatic grammar induction and parsing free text: a transformation-based approach. In Proceedings of the 31st Meeting of the Association of Computational Linguistics, Columbus, Ohio.]]

Digital Library

[3]

{Brill 93a} E. Brill 1993. A corpus-based approach to language learning. Ph.D. Dissertation, Department of Computer and Information Science, University of Pennsylvania.]]

Digital Library

[4]

{Charniak et al. 93} E. Charniak, C. Hendrickson, N. Jacobson, and M. Perkowitz. 1993. Equations for part-of-speech tagging. In Proceedings of Conference of the American Association for Artificial Intelligence (AAAI), Washington, D.C.]]

[5]

{Church 88} K. Church. 1988. A stochastic parts program and noun phrase parser for unrestricted text. In Proceedings of the Second Conference on Applied Natural Language Processing, Austin, Texas.]]

Digital Library

[6]

{Cutting et al. 92} D. Cutting, J. Kupiec, J. Pedersen, and P. Sibun. 1992. A practical part-of-speech tagger In Proceedings of the Third Conference on Applied Natural Language Processing, Trento, Italy.]]

Digital Library

[7]

{DeRose 88} S. DeRose 1988. Grammatical category disambiguation by statistical optimization. Computational Linguistics, Volume 14.]]

Digital Library

[8]

{DeMarcken 90} C. DeMarcken. 1990. Parsing the LOB corpus. In Proceedings of the 1990 Conference of the Association for Computational Linguistics.]]

Digital Library

[9]

{Harris 62} Z. Harris. 1962. String Analysis of Language Structure, Mouton and Co., The Hague.]]

[10]

{Klein and Simmons 63} S. Klein and R. Simmons. 1963. A computational approach to grammatical coding of English words. JACM, Volume 10.]]

Digital Library

[11]

{Jelinek 85} F. Jelinek. 1985. Markov source modeling of text generation. In Impact of Processing Techniques on Communication. J. Skwirzinski, ed., Dordrecht.]]

[12]

{Kupiec 92} J. Kupiec. 1992. Robust part-of-speech tagging using a hidden Markov model. Computer Speech and Language.]]

[13]

{Marcus et al. 93} M. Marcus, B. Santorini, and M. Marcinkiewicz. 1993. Building a large annotated corpus of English: the Penn Treebank. Computational Linguistics, Volume 19.]]

Digital Library

[14]

{Merialdo 91} B. Merialdo. 1991. Tagging text with a probabilistic model. In IEEE International Conference on Acoustics, Speech and Signal Processing.]]

Digital Library

[15]

{Miller 90} G. Miller. 1990. WordNet: an on-line lexical database. International Journal of Lexicography.]]

[16]

{Su et al. 92} K. Su, M. Wu, and J. Chang. 1992. A new quantitative quality measure for machine translation Systems. In Proceedings of COLING-92, Nantes, France.]]

Digital Library

[17]

{Weischedel et al. 93} R. Weischedel, M. Meteer, R. Schwartz, L. Ramshaw, and J. Palmucci. 1993. Coping with ambiguity and unknown words through probabilistic models Computational Linguistics, Volume 19.]]

Digital Library

Cited By

Khan WDaud ANasir JAmjad TArafat SAljohani NAlotaibi F(2019)Urdu part of speech tagging using conditional random fieldsLanguage Resources and Evaluation10.1007/s10579-018-9439-653:3(331-362)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s10579-018-9439-6
Nováček VSmrž P(2006)Empirical merging of ontologiesProceedings of the 3rd European conference on The Semantic Web: research and applications10.1007/11762256_8(65-79)Online publication date: 11-Jun-2006
https://dl.acm.org/doi/10.1007/11762256_8
van Delden SGomez F(2004)Retrieving NASA problem reportsData & Knowledge Engineering10.1016/S0169-023X(03)00106-X48:2(231-246)Online publication date: 1-Feb-2004
https://dl.acm.org/doi/10.1016/S0169-023X%2803%2900106-X
Show More Cited By

Recommendations

Transformation-based error-driven learning and natural language processing: a case study in part-of-speech tagging

Recently, there has been a rebirth of empiricism in the field of natural language processing. Manual encoding of linguistic information is being challenged by automated corpus-based learning as a method of providing a natural language processing system ...
Error-driven HMM-based chunk tagger with context-dependent lexicon
EMNLP '00: Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13

This paper proposes a new error-driven HMM-based text chunk tagger with context-dependent lexicon. Compared with standard HMM-based tagger, this tagger uses a new Hidden Markov Modelling approach which incorporates more contextual information into a ...
A robust transformation-based learning approach using ripple down rules for part-of-speech tagging

In this paper, we propose a new approach to construct a system of transformation rules for the Part-of-Speech (POS) tagging task. Our approach is based on an incremental knowledge acquisition method where rules are stored in an exception structure and new ...

Comments

Information & Contributors

Information

Published In

cover image DL Hosted proceedings

HLT '94: Proceedings of the workshop on Human Language Technology

March 1994

479 pages

ISBN:1558603573

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 08 March 1994

Qualifiers

Article

Acceptance Rates

Overall Acceptance Rate 240 of 768 submissions, 31%

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

4
Total Citations
View Citations
219
Total Downloads

Downloads (Last 12 months)27
Downloads (Last 6 weeks)4

Reflects downloads up to 22 Sep 2024

Other Metrics

View Author Metrics

Citations

Cited By

Khan WDaud ANasir JAmjad TArafat SAljohani NAlotaibi F(2019)Urdu part of speech tagging using conditional random fieldsLanguage Resources and Evaluation10.1007/s10579-018-9439-653:3(331-362)Online publication date: 1-Sep-2019
https://dl.acm.org/doi/10.1007/s10579-018-9439-6
Nováček VSmrž P(2006)Empirical merging of ontologiesProceedings of the 3rd European conference on The Semantic Web: research and applications10.1007/11762256_8(65-79)Online publication date: 11-Jun-2006
https://dl.acm.org/doi/10.1007/11762256_8
van Delden SGomez F(2004)Retrieving NASA problem reportsData & Knowledge Engineering10.1016/S0169-023X(03)00106-X48:2(231-246)Online publication date: 1-Feb-2004
https://dl.acm.org/doi/10.1016/S0169-023X%2803%2900106-X
Ries KWaibel A(2001)Activity detection for information access to oral communicationProceedings of the first international conference on Human language technology research10.3115/1072133.1072134(1-6)Online publication date: 18-Mar-2001
https://dl.acm.org/doi/10.3115/1072133.1072134

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