Experimental Study on a Two Phase Method for Biomedical Named Entity Recognition
Summary :
In this paper, we describe a two-phase method for biomedical named entity recognition consisting of term boundary detection and biomedical category labeling. The term boundary detection can be defined as a task to assign label sequences to a given sentence, and biomedical category labeling can be viewed as a local classification problem which does not need knowledge of the labels of other named entities in a sentence. The advantage of dividing the recognition process into two phases is that we can measure the effectiveness of models at each phase and select separately the appropriate model for each subtask. In order to obtain a better performance in biomedical named entity recognition, we conducted comparative experiments using several learning methods at each phase. Moreover, results by these machine learning based models are refined by rule-based postprocessing. We tested our methods on the JNLPBA 2004 shared task and the GENIA corpus.
- Publication
- IEICE TRANSACTIONS on Information Vol.E90-D No.7 pp.1103-1110
- Publication Date
- 2007/07/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1093/ietisy/e90-d.7.1103
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
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Seonho KIM, Juntae YOON, "Experimental Study on a Two Phase Method for Biomedical Named Entity Recognition" in IEICE TRANSACTIONS on Information,
vol. E90-D, no. 7, pp. 1103-1110, July 2007, doi: 10.1093/ietisy/e90-d.7.1103.
Abstract: In this paper, we describe a two-phase method for biomedical named entity recognition consisting of term boundary detection and biomedical category labeling. The term boundary detection can be defined as a task to assign label sequences to a given sentence, and biomedical category labeling can be viewed as a local classification problem which does not need knowledge of the labels of other named entities in a sentence. The advantage of dividing the recognition process into two phases is that we can measure the effectiveness of models at each phase and select separately the appropriate model for each subtask. In order to obtain a better performance in biomedical named entity recognition, we conducted comparative experiments using several learning methods at each phase. Moreover, results by these machine learning based models are refined by rule-based postprocessing. We tested our methods on the JNLPBA 2004 shared task and the GENIA corpus.
URL: https://global.ieice.org/en_transactions/information/10.1093/ietisy/e90-d.7.1103/_p
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@ARTICLE{e90-d_7_1103,
author={Seonho KIM, Juntae YOON, },
journal={IEICE TRANSACTIONS on Information},
title={Experimental Study on a Two Phase Method for Biomedical Named Entity Recognition},
year={2007},
volume={E90-D},
number={7},
pages={1103-1110},
abstract={In this paper, we describe a two-phase method for biomedical named entity recognition consisting of term boundary detection and biomedical category labeling. The term boundary detection can be defined as a task to assign label sequences to a given sentence, and biomedical category labeling can be viewed as a local classification problem which does not need knowledge of the labels of other named entities in a sentence. The advantage of dividing the recognition process into two phases is that we can measure the effectiveness of models at each phase and select separately the appropriate model for each subtask. In order to obtain a better performance in biomedical named entity recognition, we conducted comparative experiments using several learning methods at each phase. Moreover, results by these machine learning based models are refined by rule-based postprocessing. We tested our methods on the JNLPBA 2004 shared task and the GENIA corpus.},
keywords={},
doi={10.1093/ietisy/e90-d.7.1103},
ISSN={1745-1361},
month={July},}
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TY - JOUR
TI - Experimental Study on a Two Phase Method for Biomedical Named Entity Recognition
T2 - IEICE TRANSACTIONS on Information
SP - 1103
EP - 1110
AU - Seonho KIM
AU - Juntae YOON
PY - 2007
DO - 10.1093/ietisy/e90-d.7.1103
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E90-D
IS - 7
JA - IEICE TRANSACTIONS on Information
Y1 - July 2007
AB - In this paper, we describe a two-phase method for biomedical named entity recognition consisting of term boundary detection and biomedical category labeling. The term boundary detection can be defined as a task to assign label sequences to a given sentence, and biomedical category labeling can be viewed as a local classification problem which does not need knowledge of the labels of other named entities in a sentence. The advantage of dividing the recognition process into two phases is that we can measure the effectiveness of models at each phase and select separately the appropriate model for each subtask. In order to obtain a better performance in biomedical named entity recognition, we conducted comparative experiments using several learning methods at each phase. Moreover, results by these machine learning based models are refined by rule-based postprocessing. We tested our methods on the JNLPBA 2004 shared task and the GENIA corpus.
ER -
English
