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Named entity recognition with a maximum entropy approach

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Hai Leong Chieu,

Hwee Tou NgAuthors Info & Claims

CONLL '03: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4

Pages 160 - 163

https://doi.org/10.3115/1119176.1119199

Published: 31 May 2003 Publication History

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Abstract

The named entity recognition (NER) task involves identifying noun phrases that are names, and assigning a class to each name. This task has its origin from the Message Understanding Conferences (MUC) in the 1990s, a series of conferences aimed at evaluating systems that extract information from natural language texts. It became evident that in order to achieve good performance in information extraction, a system needs to be able to recognize names. A separate subtask on NER was created in MUC-6 and MUC-7 (Chinchor, 1998).

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Hai Leong Chieu and Hwee Tou Ng. 2002a. A Maximum Entropy Approach to Information Extraction from Semi-Structured and Free Text. In Proceedings of the Eighteenth National Conference on Artificial Intelligence, pages 786--791.

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Hai Leong Chieu and Hwee Tou Ng. 2002b. Named Entity Recognition: A Maximum Entropy Approach Using Global Information. In Proceedings of the Nineteenth International Conference on Computational Linguistics, pages 190--196.

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Cited By

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Luo JLiu YHou M(2022)A Novel Chinese Resume Named Entity Recognition Model Based on Lexical EnhancementProceedings of the 2022 11th International Conference on Computing and Pattern Recognition10.1145/3581807.3581856(341-346)Online publication date: 17-Nov-2022
https://dl.acm.org/doi/10.1145/3581807.3581856
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Chen LMoschitti A(2019)Transfer learning for sequence labeling using source model and target dataProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33016260(6260-6267)Online publication date: 27-Jan-2019
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CONLL '03: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4

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CONLL '03: Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4

May 2003

213 pages

Conference Chairs:
Walter Daelemans
University of Antwerp and Tilburg University
,
Miles Osborne
University of Edinburgh

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Association for Computational Linguistics

United States

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Published: 31 May 2003

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Luo JLiu YHou M(2022)A Novel Chinese Resume Named Entity Recognition Model Based on Lexical EnhancementProceedings of the 2022 11th International Conference on Computing and Pattern Recognition10.1145/3581807.3581856(341-346)Online publication date: 17-Nov-2022
https://dl.acm.org/doi/10.1145/3581807.3581856
Yu YWang YMu JLi WJiao SWang ZLv PZhu Y(2022)Chinese mineral named entity recognition based on BERT modelExpert Systems with Applications: An International Journal10.1016/j.eswa.2022.117727206:COnline publication date: 15-Nov-2022
https://dl.acm.org/doi/10.1016/j.eswa.2022.117727
Chen LMoschitti A(2019)Transfer learning for sequence labeling using source model and target dataProceedings of the Thirty-Third AAAI Conference on Artificial Intelligence and Thirty-First Innovative Applications of Artificial Intelligence Conference and Ninth AAAI Symposium on Educational Advances in Artificial Intelligence10.1609/aaai.v33i01.33016260(6260-6267)Online publication date: 27-Jan-2019
https://dl.acm.org/doi/10.1609/aaai.v33i01.33016260
Gao MXiao QWu SDeng K(2019)An Attention-Based ID-CNNs-CRF Model for Named Entity Recognition on Clinical Electronic Medical RecordsArtificial Neural Networks and Machine Learning – ICANN 2019: Workshop and Special Sessions10.1007/978-3-030-30493-5_25(231-242)Online publication date: 17-Sep-2019
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Ekbal ASaha S(2013)Combining feature selection and classifier ensemble using a multiobjective simulated annealing approachSoft Computing - A Fusion of Foundations, Methodologies and Applications10.1007/s00500-012-0885-617:1(1-16)Online publication date: 1-Jan-2013
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Shin MLee HHong M(2012)A hybrid approach to gene ranking using gene relation networks derived from literature for the identification of disease gene markersInternational Journal of Data Mining and Bioinformatics10.1504/IJDMB.2012.0492506:3(239-254)Online publication date: 1-Sep-2012
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Kurata GItoh NNishimura MSethy ARamabhadran B(2012)Leveraging word confusion networks for named entity modeling and detection from conversational telephone speechSpeech Communication10.1016/j.specom.2011.11.00254:3(491-502)Online publication date: 1-Mar-2012
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Jung J(2012)Online named entity recognition method for microtexts in social networking servicesExpert Systems with Applications: An International Journal10.1016/j.eswa.2012.01.13639:9(8066-8070)Online publication date: 1-Jul-2012
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Plachouras VRivière MVazirgiannis M(2012)Named entity recognition and identification for finding the owner of a home pageProceedings of the 16th Pacific-Asia conference on Advances in Knowledge Discovery and Data Mining - Volume Part I10.1007/978-3-642-30217-6_46(554-565)Online publication date: 29-May-2012
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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
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Maximum entropy models for named entity recognition

Two-stage approach to named entity recognition using Wikipedia and DBpedia

A maximum entropy approach to named entity recognition

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