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Efficient support vector classifiers for named entity recognition

Authors:

Hideki Isozaki,

Hideto KazawaAuthors Info & Claims

COLING '02: Proceedings of the 19th international conference on Computational linguistics - Volume 1

Pages 1 - 7

https://doi.org/10.3115/1072228.1072282

Published: 24 August 2002 Publication History

Abstract

Named Entity (NE) recognition is a task in which proper nouns and numerical information are extracted from documents and are classified into categories such as person, organization, and date. It is a key technology of Information Extraction and Open-Domain Question Answering. First, we show that an NE recognizer based on Support Vector Machines (SVMs) gives better scores than conventional systems. However, off-the-shelf SVM classifiers are too inefficient for this task. Therefore, we present a method that makes the system substantially faster. This approach can also be applied to other similar tasks such as chunking and part-of-speech tagging. We also present an SVM-based feature selection method and an efficient training method.

References

[1]

James Allen. 1995. Natural Language Understanding 2nd. Ed. Benjamin Cummings.]]

Digital Library

[2]

Andrew Borthwick. 1999. A Maximum Entropy Approach to Named Entity Recognition. Ph.D. thesis, New York University.]]

Digital Library

[3]

Chris J. C. Burges and Bernhard Schölkopf. 1997. Improving speed and accuracy of support vector learning machines. In Advances in Neural Information Processing Systems 9, pages 375--381.]]

[4]

Tom Downs, Kevin E. Gates, and Annette Masters. 2001. Exact simplification of support vector solutions. Journal of Machine Learning Research, 2:293--297.]]

Digital Library

[5]

Hideki Isozaki. 2001. Japanese named entity recognition based on a simple rule generator and decision tree learning. In Proceedings of Association for Computational Linguistics, pages 306--313.]]

Digital Library

[6]

Tommi S. Jaakkola and David Haussler. 1998. Exploiting generative models in discriminative classifiers. In M. S. Kearns, S. A. Solla, and D. A. Cohn, editors, Advances in Neural Information Processing Systems 11. MIT Press.]]

Digital Library

[7]

Thorsten Joachims. 1998. Text categorization with support vector machines: Learning with many relevant features. In Proceedings of the European Conference on Machine Learning.]]

Digital Library

[8]

Thorsten Joachims. 1999. Making large-scale support vector machine learning practical. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods, chapter 16, pages 170--184. MIT Press.]]

Digital Library

[9]

Taku Kudo and Yuji Matsumoto. 2001. Chunking with support vector machines. In Proceedings of NAACL, pages 192--199.]]

Digital Library

[10]

Tetsuji Nakagawa, Taku Kudoh, and Yuji Matsumoto. 2001. Unknown word guessing and part-of-speech tagging using support vector machines. In Proceedings of the Sixth Natural Language Processing Pacific Rim Symposium, pages 325--331.]]

[11]

Edgar E. Osuna and Federico Girosi. 1999. Reducing the run-time complexity in support vector machines. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods, chapter 16, pages 271--283. MIT Press.]]

Digital Library

[12]

John C. Platt, Nello Cristiani, and John Shawe-Taylor. 2000. Large margin DAGs for multiclass classification. In Advances in Neural Information Processing Systems 12, pages 547--553. MIT Press.]]

[13]

John C. Platt. 1999. Fast training of support vector machines using sequential minimal optimization. In B. Schölkopf, C. J. C. Burges, and A. J. Smola, editors, Advances in Kernel Methods, chapter 12, pages 185--208. MIT Press.]]

Digital Library

[14]

John C. Platt. 2000. Probabilities for SV machines. In A. J. Smola, P. L. Bartlett, B. Schölkopf, and D. Schuurmans, editors, Advances in Large Margin Classifiers, chapter 5, pages 61--71. MIT Press.]]

[15]

Friedhelm Schwenker. 2001. Solving multi-class pattern recognition problems with tree-structured support vector machines. In B. Radig and S. Florczyk, editors, Pattern Recognition, Proceedings of the 23rd Symposium, number 2191 in LNCS, pages 283--290. Springer.]]

Digital Library

[16]

Satoshi Sekine and Yoshio Eriguchi. 2000. Japanese named entity extraction evaluation --- analysis of results ---. In Proceedings of 18th International Conference on Computational Linguistics, pages 1106--1110.]]

Digital Library

[17]

Satoshi Sekine, Ralph Grishman, and Hiroyuki Shinnou. 1998. A decision tree method for finding and classifying names in Japanese texts. In Proceedings of the Sixth Workshop on Very Large Corpora.]]

[18]

Hiroshi Shimodaira, Ken-ichi Noma, Mitsuru Naka, and Shigeki Sagayama. 2001. Support vector machine with dynamic time-alignment kernel for speech recognition. In Proceedings of Eurospeech, pages 1841--1844.]]

[19]

Koji Tsuda, M. Kawanabe, G. Rätsch, S. Sonnenburg, and K. Müller. 2001. A new discriminative kernel from probabilistic models. In Advances in Newral Information Processing Systems 14.]]

[20]

Kiyotaka Uchimoto, Qing Ma, Masaki Murata, Hiromi Ozaku, Masao Utiyama, and Hitoshi Isahara. 2000. Named entity extraction based on a maximum entropy model and transformation rules (in Japanese). Journal of Natural Language Processing, 7(2):63--90.]]

[21]

Takehito Utsuro, Manabu Sassano, and Kiyotaka Uchimoto. 2001. Learning to combine outputs of multiple Japanese named entity extractors (in Japanese). In IPSJ SIG notes NL-144-5.]]

[22]

Vladimir N. Vapnik. 1995. The Nature of Statistical Learning Theory. Springer.]]

Digital Library

[23]

E. M. Voorhees and D. K. Harman, editors. 2000. Proceedings of the 9th Text Retrieval Conference.]]

[24]

Hiroyasu Yamada and Yuji Matsumoto. 2001. Applying support vector machine to multi-class classification problems (in Japanese). In IPSJ SIG Notes NL-146-6.]]

[25]

Hiroyasu Yamada, Taku Kudoh, and Yuji Matsumoto. 2001. Japanese named entity extraction using support vector machines (in Japanese). In IPSJ SIG Notes NL-142-17.]]

Cited By

Yan JZong CXu J(2023)Combination of Loss-based Active Learning and Semi-supervised Learning for Recognizing Entities in Chinese Electronic Medical RecordsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358831422:5(1-19)Online publication date: 20-Mar-2023
https://dl.acm.org/doi/10.1145/3588314
Runmei ZLulu LLei YJingjing LWeiyi XWeiwei CZhong C(2022)Chinese Named Entity Recognition Method Combining ALBERT and a Local Adversarial Training and Adding Attention MechanismInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.31394618:1(1-20)Online publication date: 15-Dec-2022
https://dl.acm.org/doi/10.4018/IJSWIS.313946
Ali WKumar RDai YKumar JTumrani S(2021)Neural Joint Model for Part-of-Speech Tagging and Entity ExtractionProceedings of the 2021 13th International Conference on Machine Learning and Computing10.1145/3457682.3457718(239-245)Online publication date: 26-Feb-2021
https://dl.acm.org/doi/10.1145/3457682.3457718
Show More Cited By

Efficient support vector classifiers for named entity recognition
1. Computing methodologies
  1. Machine learning
    1. Learning paradigms
      1. Supervised learning
    2. Machine learning approaches
2. Hardware
  1. Power and energy
    1. Power estimation and optimization

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COLING '02: Proceedings of the 19th international conference on Computational linguistics - Volume 1

August 2002

1184 pages

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Association for Computational Linguistics

United States

Publication History

Published: 24 August 2002

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Overall Acceptance Rate 1,537 of 1,537 submissions, 100%

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Cited By

Yan JZong CXu J(2023)Combination of Loss-based Active Learning and Semi-supervised Learning for Recognizing Entities in Chinese Electronic Medical RecordsACM Transactions on Asian and Low-Resource Language Information Processing10.1145/358831422:5(1-19)Online publication date: 20-Mar-2023
https://dl.acm.org/doi/10.1145/3588314
Runmei ZLulu LLei YJingjing LWeiyi XWeiwei CZhong C(2022)Chinese Named Entity Recognition Method Combining ALBERT and a Local Adversarial Training and Adding Attention MechanismInternational Journal on Semantic Web & Information Systems10.4018/IJSWIS.31394618:1(1-20)Online publication date: 15-Dec-2022
https://dl.acm.org/doi/10.4018/IJSWIS.313946
Ali WKumar RDai YKumar JTumrani S(2021)Neural Joint Model for Part-of-Speech Tagging and Entity ExtractionProceedings of the 2021 13th International Conference on Machine Learning and Computing10.1145/3457682.3457718(239-245)Online publication date: 26-Feb-2021
https://dl.acm.org/doi/10.1145/3457682.3457718
Liu JYe LZhang HGuo X(2020)Named entity recognition of legal judgment based on small-scale labeled dataProceedings of the 2020 International Conference on Cyberspace Innovation of Advanced Technologies10.1145/3444370.3444626(549-555)Online publication date: 4-Dec-2020
https://dl.acm.org/doi/10.1145/3444370.3444626
Bao YAn YCheng ZJiao RZhu CLeng FWang SWu PYu G(2020)Named Entity Recognition in Aircraft Design Field Based on Deep LearningWeb Information Systems and Applications10.1007/978-3-030-60029-7_31(333-340)Online publication date: 23-Sep-2020
https://dl.acm.org/doi/10.1007/978-3-030-60029-7_31
Wang WChang LBin CXuan WChen WLi LTavares JXu Z(2019)ESN-NERProceedings of the International Conference on Artificial Intelligence, Information Processing and Cloud Computing10.1145/3371425.3371436(1-8)Online publication date: 19-Dec-2019
https://dl.acm.org/doi/10.1145/3371425.3371436
Truong TDao ANguyen LDinh D(2018)Improving Named Entity Recognition of English and Vietnamese Languages using Bilingual ConstraintsProceedings of the 2nd International Conference on Natural Language Processing and Information Retrieval10.1145/3278293.3278305(70-75)Online publication date: 7-Sep-2018
https://dl.acm.org/doi/10.1145/3278293.3278305
Kenekayoro P(2018)Identifying named entities in academic biographies with supervised learningScientometrics10.1007/s11192-018-2797-4116:2(751-765)Online publication date: 1-Aug-2018
https://dl.acm.org/doi/10.1007/s11192-018-2797-4
Chodpathumwan YVakilian ATermehchy ANayyeri A(2018)Cost-effective conceptual design using taxonomiesThe VLDB Journal — The International Journal on Very Large Data Bases10.1007/s00778-018-0501-127:3(369-394)Online publication date: 1-Jun-2018
https://dl.acm.org/doi/10.1007/s00778-018-0501-1
Gorla SMurthy NMalapati A(2017)A Comparative Study of Named Entity Recognition for TeluguProceedings of the 9th Annual Meeting of the Forum for Information Retrieval Evaluation10.1145/3158354.3158358(21-24)Online publication date: 8-Dec-2017
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