The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Memandangkan soalan dan satu set jawapan calonnya, tugas pengesahan jawapan (AV) bertujuan untuk mengembalikan nilai Boolean yang menunjukkan sama ada jawapan calon yang diberikan adalah jawapan yang betul kepada soalan itu. Tidak seperti karya sebelumnya, kertas kerja ini membentangkan model tanpa pengawasan, yang dipanggil model U, untuk AV. Pendekatan ini menganggap AV sebagai tugas pengelasan dan menyiasat sejauh mana keberkesanan penggunaan redundansi Web ke dalam seni bina yang dicadangkan. Keputusan eksperimen dengan set ujian factoid TREC dan set ujian Cina menunjukkan bahawa model U yang dicadangkan dengan maklumat lebihan adalah sangat berkesan untuk AV. Sebagai contoh, markah atas@1/mrr@5 pada trek TREC05 dan 06 adalah masing-masing 40.1/51.5% dan 35.8/47.3%. Tambahan pula, percubaan perbandingan silang model menunjukkan bahawa model U adalah yang terbaik antara model berasaskan redundansi yang dipertimbangkan. Walaupun dibandingkan dengan pendekatan berasaskan sintaks, pendekatan pembelajaran mesin yang diselia dan pendekatan berasaskan corak, model U menunjukkan prestasi yang lebih baik.
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Salinan
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
Salinan
@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},}
Salinan
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 -