Article

Free access

Chunking with maximum entropy models

Author:

ConLL '00: Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7

Pages 139 - 141

https://doi.org/10.3115/1117601.1117634

Published: 13 September 2000 Publication History

PDF eReader

Abstract

In this paper I discuss a first attempt to create a text chunker using a Maximum Entropy model. The first experiments, implementing classifiers that tag every word in a sentence with a phrase-tag using very local lexical information, part-of-speech tags and phrase tags of surrounding words, give encouraging results.

References

[1]

Adam L. Berger, Stephen A. Della Pietra, and Vincent J. Della Pietra. 1996. A maximum entropy approach to natural language processing. Computational Linguistics, 22(1).

Digital Library

Google Scholar

[2]

Luc Dehaspe. 1997. Maximum entropy modeling with clausal constraints. In Proceedings of the 7th International Workshop on Inductive Logic Programming.

Digital Library

Google Scholar

[3]

S. Della Pietra, V. Della Pietra, and J. Lafferty. 1997. Inducing features from random fields. IEEE Transactions on Patterns Analysis and Machine Intelligence, 19(4).

Digital Library

Google Scholar

[4]

E. T. Jaynes. 1957. Information theory and statistical mechanics. Physical Review, 108:171--190.

Crossref

Google Scholar

[5]

Andrei Mikheev. forthcoming. Feature lattices and maximum entropy models. Journal of Machine Learning.

Google Scholar

[6]

Marcia Muñoz, Vasin Punyakanok, Dan Roth, and Dav Zimak. 1999. A learning approach to shallow parsing. In Proceedings of EMNLP-WVLC'99.

Google Scholar

[7]

Adwait Ratnaparkhi. 1997. A linear observed time statistical parser based on maximum entropy models. In Proceedings of the Second Conference on Empirical Methods in Natural Language Processing, Brown University, Providence, Rhode Island.

Google Scholar

[8]

Adwait Ratnaparkhi. 1998. Maximum Entropy Models for Natural Language Ambiguity Resolution. Ph.D. thesis, UPenn.

Digital Library

Google Scholar

[9]

Sven Eric Ristad. 1998. Maximum entropy modelling toolkit. Technical report.

Google Scholar

[10]

Ronald Rosenfeld. 1994. Adaptive Statistical Language Modelling: A Maximum Entropy Approach. Ph.D. thesis, Carnegy Mellon University.

Google Scholar

[11]

Erik F. Tjong Kim Sang and Jorn Veenstra. 1999. Strategy and tactics: a model for language production. In Ninth Conference of the European Chapter of the Association for Computational Linguistics, University of Bergen, Bergen, Norway.

Google Scholar

Cited By

View all

Sharipbay ARazakhova BMukanova AYergesh BYelibayeva GHoballah IZeineddine HAljawarneh SLara J(2019)Syntax parsing model of Kazakh simple sentencesProceedings of the Second International Conference on Data Science, E-Learning and Information Systems10.1145/3368691.3368745(1-5)Online publication date: 2-Dec-2019
https://dl.acm.org/doi/10.1145/3368691.3368745
Umemoto KSong RNie JXie XTanaka KRui Y(2017)Search by Screenshots for Universal Article Clipping in Mobile AppsACM Transactions on Information Systems10.1145/309110735:4(1-29)Online publication date: 23-Jun-2017
https://dl.acm.org/doi/10.1145/3091107
Zhai KKozareva ZHu YLi QGuo WPerego RSebastiani FAslam JRuthven IZobel J(2016)Query to KnowledgeProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2911495(255-264)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2911495
Show More Cited By

Chunking with maximum entropy models
1. Computing methodologies
  1. Artificial intelligence
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

Recommendations

Maximum entropy models for word sense disambiguation
COLING '02: Proceedings of the 19th international conference on Computational linguistics - Volume 1

A maximum entropy-based word sense disambiguation system is presented, consisting of individual word experts that are trained on both labeled and partially labeled corpora. The classification probabilities from the individual word experts are integrated ...
Incorporating linguistic structure into maximum entropy language models
Abstract
In statistical language models, how to integrate diverse linguistic knowledge in a general framework for long-distance dependencies is a challenging issue. In this paper, an improved language model incorporating linguistic structure into maximum ...
Noun phrase chunking in Hebrew: influence of lexical and morphological features
ACL-44: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics

We present a method for Noun Phrase chunking in Hebrew. We show that the traditional definition of base-NPs as non-recursive noun phrases does not apply in Hebrew, and propose an alternative definition of Simple NPs. We review syntactic properties of ...

Comments

Information & Contributors

Information

Published In

ConLL '00: Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7

September 2000

238 pages

Publisher

Association for Computational Linguistics

United States

Publication History

Published: 13 September 2000

Qualifiers

Article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

20
Total Citations
View Citations
444
Total Downloads

Downloads (Last 12 months)60
Downloads (Last 6 weeks)4

Reflects downloads up to 25 Dec 2024

Other Metrics

View Author Metrics

Citations

Cited By

View all

Sharipbay ARazakhova BMukanova AYergesh BYelibayeva GHoballah IZeineddine HAljawarneh SLara J(2019)Syntax parsing model of Kazakh simple sentencesProceedings of the Second International Conference on Data Science, E-Learning and Information Systems10.1145/3368691.3368745(1-5)Online publication date: 2-Dec-2019
https://dl.acm.org/doi/10.1145/3368691.3368745
Umemoto KSong RNie JXie XTanaka KRui Y(2017)Search by Screenshots for Universal Article Clipping in Mobile AppsACM Transactions on Information Systems10.1145/309110735:4(1-29)Online publication date: 23-Jun-2017
https://dl.acm.org/doi/10.1145/3091107
Zhai KKozareva ZHu YLi QGuo WPerego RSebastiani FAslam JRuthven IZobel J(2016)Query to KnowledgeProceedings of the 39th International ACM SIGIR conference on Research and Development in Information Retrieval10.1145/2911451.2911495(255-264)Online publication date: 7-Jul-2016
https://dl.acm.org/doi/10.1145/2911451.2911495
Endrédy I(2015)Improving Chunker Performance Using a Web-Based Semi-automatic Training Data Analysis ToolHuman Language Technology. Challenges for Computer Science and Linguistics10.1007/978-3-319-93782-3_21(290-303)Online publication date: 17-Nov-2015
https://dl.acm.org/doi/10.1007/978-3-319-93782-3_21
Bach NMinh NOanh TShimazu A(2013)A Two-Phase Framework for Learning Logical Structures of Paragraphs in Legal ArticlesACM Transactions on Asian Language Information Processing10.1145/2425327.242533012:1(1-32)Online publication date: 1-Mar-2013
https://dl.acm.org/doi/10.1145/2425327.2425330
Atkinson JMatamala J(2012)Evolutionary Shallow Natural Language ParsingComputational Intelligence10.1111/j.1467-8640.2012.00412.x28:2(156-175)Online publication date: 1-May-2012
https://dl.acm.org/doi/10.1111/j.1467-8640.2012.00412.x
Putthividhya DHu JMerlo PBarzilay RJohnson M(2011)Bootstrapped named entity recognition for product attribute extractionProceedings of the Conference on Empirical Methods in Natural Language Processing10.5555/2145432.2145598(1557-1567)Online publication date: 27-Jul-2011
https://dl.acm.org/doi/10.5555/2145432.2145598
Dong ZZhao T(2010)A deterministic method to predict phrase boundaries of a syntactic treeProceedings of the Advanced intelligent computing theories and applications, and 6th international conference on Intelligent computing10.5555/1881227.1881326(649-656)Online publication date: 18-Aug-2010
https://dl.acm.org/doi/10.5555/1881227.1881326
Lee YWu Y(2007)A robust multilingual portable phrase chunking systemExpert Systems with Applications: An International Journal10.1016/j.eswa.2006.06.02233:3(590-599)Online publication date: 1-Oct-2007
https://dl.acm.org/doi/10.1016/j.eswa.2006.06.022
Zhang QWeng FFeng Z(2006)A progressive feature selection algorithm for ultra large feature spacesProceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics10.3115/1220175.1220246(561-568)Online publication date: 17-Jul-2006
https://dl.acm.org/doi/10.3115/1220175.1220246
Show More Cited By

View Options

View options

PDF

View or Download as a PDF file.

PDF

eReader

View online with eReader.

eReader

Login options

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

Abstract

References

Cited By

Recommendations

Maximum entropy models for word sense disambiguation

Incorporating linguistic structure into maximum entropy language models

Noun phrase chunking in Hebrew: influence of lexical and morphological features

Comments

Information

Published In

Publisher

Publication History

Qualifiers

Contributors

Other Metrics

Bibliometrics

Article Metrics

Other Metrics

Citations

Cited By

View options

PDF

eReader

Login options

Full Access

Figures

Other

Share

Share this Publication link

Share on social media

Affiliations