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Named Entity Recognition using an HMM-based Chunk Tagger

by: GuoDong Zhou, and Jian Su

In: Proc. 40th Annual Meeting of the Association for Computational Linguistics ACL 2002 (2002) .

Abstract

This paper proposes a Hidden Markov Model HMM and an HMM-based chunk tagger, from which a named entity NE recognition NER system is built to recognize and classify names, times and numerical quantities. Through the HMM, our system is able to apply and integrate four types of internal and external evidences: 1 simple deterministic internal feature of the words, such as capitalization and digitalization; 2 internal semantic feature of important triggers; 3 internal gazetteer feature; 4 external macro context feature. In this way, the NER problem can be resolved effectively. Evaluation of our system on MUC-6 and MUC-7 English NE tasks achieves F-measures of 96.6% and 94.1% respectively. It shows that the performance is significantly better than reported by any other machine-learning system. Moreover, the performance is even consistently better than those based on handcrafted rules.

BibTeX record

@inproceedings{Zhou:2002,
  abstract = {This paper proposes a Hidden Markov Model (HMM) and an HMM-based chunk tagger, from which a named entity (NE) recognition (NER) system is built to recognize and classify names, times and numerical quantities. Through the HMM, our system is able to apply and integrate four types of internal and external evidences: 1) simple deterministic internal feature of the words, such as capitalization and digitalization; 2) internal semantic feature of important triggers; 3) internal gazetteer feature; 4) external macro context feature. In this way, the NER problem can be resolved effectively. Evaluation of our system on MUC-6 and MUC-7 English NE tasks achieves F-measures of 96.6% and 94.1% respectively. It shows that the performance is significantly better than reported by any other machine-learning system. Moreover, the performance is even consistently better than those based on handcrafted rules.},
  added-at = {2007-12-14T02:48:42.000+0100},
  author = {Zhou, GuoDong and Su, Jian},
  biburl = {http://www.bibsonomy.org/bibtex/2753c74267baabe14fe6d420b78043998/diego_ma},
  booktitle = {Proc. 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002)},
  interhash = {9c075941b088aa183059d9db2a6746c9},
  intrahash = {753c74267baabe14fe6d420b78043998},
  keywords = {named_entities machine_learning},
  timestamp = {2007-12-14T02:48:42.000+0100},
  title = {Named Entity Recognition using an {HMM}-based Chunk Tagger},
  year = 2002
}

Endnote record

%0 Conference Paper
%1 Zhou:2002
%A Zhou, GuoDong
%A Su, Jian
%B Proc. 40th Annual Meeting of the Association for Computational Linguistics (ACL 2002)
%D 2002
%K 
%T Named Entity Recognition using an {HMM}-based Chunk Tagger
%X This paper proposes a Hidden Markov Model (HMM) and an HMM-based chunk tagger, from which a named entity (NE) recognition (NER) system is built to recognize and classify names, times and numerical quantities. Through the HMM, our system is able to apply and integrate four types of internal and external evidences: 1) simple deterministic internal feature of the words, such as capitalization and digitalization; 2) internal semantic feature of important triggers; 3) internal gazetteer feature; 4) external macro context feature. In this way, the NER problem can be resolved effectively. Evaluation of our system on MUC-6 and MUC-7 English NE tasks achieves F-measures of 96.6% and 94.1% respectively. It shows that the performance is significantly better than reported by any other machine-learning system. Moreover, the performance is even consistently better than those based on handcrafted rules.