IEICE TRANSACTIONS on Information
Voting-Based Ensemble Classifiers to Detect Hedges and Their Scopes in Biomedical Texts
Summary :
Previous studies of pattern recognition have shown that classifiers ensemble approaches can lead to better recognition results. In this paper, we apply the voting technique for the CoNLL-2010 shared task on detecting hedge cues and their scope in biomedical texts. Six machine learning-based systems are combined through three different voting schemes. We demonstrate the effectiveness of classifiers ensemble approaches and compare the performance of three different voting schemes for hedge cue and their scope detection. Experiments on the CoNLL-2010 evaluation data show that our best system achieves an F-score of 87.49% on hedge detection task and 60.87% on scope finding task respectively, which are significantly better than those of the previous systems.
- Publication
- IEICE TRANSACTIONS on Information Vol.E94-D No.10 pp.1989-1997
- Publication Date
- 2011/10/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E94.D.1989
- Type of Manuscript
- PAPER
- Category
- Artificial Intelligence, Data Mining
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Huiwei ZHOU, Xiaoyan LI, Degen HUANG, Yuansheng YANG, Fuji REN, "Voting-Based Ensemble Classifiers to Detect Hedges and Their Scopes in Biomedical Texts" in IEICE TRANSACTIONS on Information,
vol. E94-D, no. 10, pp. 1989-1997, October 2011, doi: 10.1587/transinf.E94.D.1989.
Abstract: Previous studies of pattern recognition have shown that classifiers ensemble approaches can lead to better recognition results. In this paper, we apply the voting technique for the CoNLL-2010 shared task on detecting hedge cues and their scope in biomedical texts. Six machine learning-based systems are combined through three different voting schemes. We demonstrate the effectiveness of classifiers ensemble approaches and compare the performance of three different voting schemes for hedge cue and their scope detection. Experiments on the CoNLL-2010 evaluation data show that our best system achieves an F-score of 87.49% on hedge detection task and 60.87% on scope finding task respectively, which are significantly better than those of the previous systems.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E94.D.1989/_p
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@ARTICLE{e94-d_10_1989,
author={Huiwei ZHOU, Xiaoyan LI, Degen HUANG, Yuansheng YANG, Fuji REN, },
journal={IEICE TRANSACTIONS on Information},
title={Voting-Based Ensemble Classifiers to Detect Hedges and Their Scopes in Biomedical Texts},
year={2011},
volume={E94-D},
number={10},
pages={1989-1997},
abstract={Previous studies of pattern recognition have shown that classifiers ensemble approaches can lead to better recognition results. In this paper, we apply the voting technique for the CoNLL-2010 shared task on detecting hedge cues and their scope in biomedical texts. Six machine learning-based systems are combined through three different voting schemes. We demonstrate the effectiveness of classifiers ensemble approaches and compare the performance of three different voting schemes for hedge cue and their scope detection. Experiments on the CoNLL-2010 evaluation data show that our best system achieves an F-score of 87.49% on hedge detection task and 60.87% on scope finding task respectively, which are significantly better than those of the previous systems.},
keywords={},
doi={10.1587/transinf.E94.D.1989},
ISSN={1745-1361},
month={October},}
Copy
TY - JOUR
TI - Voting-Based Ensemble Classifiers to Detect Hedges and Their Scopes in Biomedical Texts
T2 - IEICE TRANSACTIONS on Information
SP - 1989
EP - 1997
AU - Huiwei ZHOU
AU - Xiaoyan LI
AU - Degen HUANG
AU - Yuansheng YANG
AU - Fuji REN
PY - 2011
DO - 10.1587/transinf.E94.D.1989
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E94-D
IS - 10
JA - IEICE TRANSACTIONS on Information
Y1 - October 2011
AB - Previous studies of pattern recognition have shown that classifiers ensemble approaches can lead to better recognition results. In this paper, we apply the voting technique for the CoNLL-2010 shared task on detecting hedge cues and their scope in biomedical texts. Six machine learning-based systems are combined through three different voting schemes. We demonstrate the effectiveness of classifiers ensemble approaches and compare the performance of three different voting schemes for hedge cue and their scope detection. Experiments on the CoNLL-2010 evaluation data show that our best system achieves an F-score of 87.49% on hedge detection task and 60.87% on scope finding task respectively, which are significantly better than those of the previous systems.
ER -
English
