IEICE TRANSACTIONS on Information
An Unsupervised Model of Redundancy for Answer Validation
Summary :
Given a question and a set of its candidate answers, the task of answer validation (AV) aims to return a Boolean value indicating whether a given candidate answer is the correct answer to the question. Unlike previous works, this paper presents an unsupervised model, called the U-model, for AV. This approach regards AV as a classification task and investigates how effectively using redundancy of the Web into the proposed architecture. Experimental results with TREC factoid test sets and Chinese test sets indicate that the proposed U-model with redundancy information is very effective for AV. For example, the top@1/mrr@5 scores on the TREC05, and 06 tracks are 40.1/51.5% and 35.8/47.3%, respectively. Furthermore, a cross-model comparison experiment demonstrates that the U-model is the best among the redundancy-based models considered. Even compared with a syntax-based approach, a supervised machine learning approach and a pattern-based approach, the U-model performs much better.
- Publication
- IEICE TRANSACTIONS on Information Vol.E93-D No.3 pp.624-634
- Publication Date
- 2010/03/01
- Publicized
- Online ISSN
- 1745-1361
- DOI
- 10.1587/transinf.E93.D.624
- Type of Manuscript
- PAPER
- Category
- Natural Language Processing
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Keyword
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Youzheng WU, Hideki KASHIOKA, Satoshi NAKAMURA, "An Unsupervised Model of Redundancy for Answer Validation" in IEICE TRANSACTIONS on Information,
vol. E93-D, no. 3, pp. 624-634, March 2010, doi: 10.1587/transinf.E93.D.624.
Abstract: Given a question and a set of its candidate answers, the task of answer validation (AV) aims to return a Boolean value indicating whether a given candidate answer is the correct answer to the question. Unlike previous works, this paper presents an unsupervised model, called the U-model, for AV. This approach regards AV as a classification task and investigates how effectively using redundancy of the Web into the proposed architecture. Experimental results with TREC factoid test sets and Chinese test sets indicate that the proposed U-model with redundancy information is very effective for AV. For example, the top@1/mrr@5 scores on the TREC05, and 06 tracks are 40.1/51.5% and 35.8/47.3%, respectively. Furthermore, a cross-model comparison experiment demonstrates that the U-model is the best among the redundancy-based models considered. Even compared with a syntax-based approach, a supervised machine learning approach and a pattern-based approach, the U-model performs much better.
URL: https://global.ieice.org/en_transactions/information/10.1587/transinf.E93.D.624/_p
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@ARTICLE{e93-d_3_624,
author={Youzheng WU, Hideki KASHIOKA, Satoshi NAKAMURA, },
journal={IEICE TRANSACTIONS on Information},
title={An Unsupervised Model of Redundancy for Answer Validation},
year={2010},
volume={E93-D},
number={3},
pages={624-634},
abstract={Given a question and a set of its candidate answers, the task of answer validation (AV) aims to return a Boolean value indicating whether a given candidate answer is the correct answer to the question. Unlike previous works, this paper presents an unsupervised model, called the U-model, for AV. This approach regards AV as a classification task and investigates how effectively using redundancy of the Web into the proposed architecture. Experimental results with TREC factoid test sets and Chinese test sets indicate that the proposed U-model with redundancy information is very effective for AV. For example, the top@1/mrr@5 scores on the TREC05, and 06 tracks are 40.1/51.5% and 35.8/47.3%, respectively. Furthermore, a cross-model comparison experiment demonstrates that the U-model is the best among the redundancy-based models considered. Even compared with a syntax-based approach, a supervised machine learning approach and a pattern-based approach, the U-model performs much better.},
keywords={},
doi={10.1587/transinf.E93.D.624},
ISSN={1745-1361},
month={March},}
Copy
TY - JOUR
TI - An Unsupervised Model of Redundancy for Answer Validation
T2 - IEICE TRANSACTIONS on Information
SP - 624
EP - 634
AU - Youzheng WU
AU - Hideki KASHIOKA
AU - Satoshi NAKAMURA
PY - 2010
DO - 10.1587/transinf.E93.D.624
JO - IEICE TRANSACTIONS on Information
SN - 1745-1361
VL - E93-D
IS - 3
JA - IEICE TRANSACTIONS on Information
Y1 - March 2010
AB - Given a question and a set of its candidate answers, the task of answer validation (AV) aims to return a Boolean value indicating whether a given candidate answer is the correct answer to the question. Unlike previous works, this paper presents an unsupervised model, called the U-model, for AV. This approach regards AV as a classification task and investigates how effectively using redundancy of the Web into the proposed architecture. Experimental results with TREC factoid test sets and Chinese test sets indicate that the proposed U-model with redundancy information is very effective for AV. For example, the top@1/mrr@5 scores on the TREC05, and 06 tracks are 40.1/51.5% and 35.8/47.3%, respectively. Furthermore, a cross-model comparison experiment demonstrates that the U-model is the best among the redundancy-based models considered. Even compared with a syntax-based approach, a supervised machine learning approach and a pattern-based approach, the U-model performs much better.
ER -
English
