An Unsupervised Model of Redundancy for Answer Validation

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概要

• 論文の詳細を見る

• 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.

• （社）電子情報通信学会の論文
• 2010-03-01

著者

• NAKAMURA Satoshi

  Spoken Language Communication Group, Knowledge Creating Communication Research Center, National Inst

• Kashioka Hideki

  Spoken Language Communication Group National Institute Of Information And Communications Technology

• WU Youzheng

  Spoken Language Communication Group, National Institute of Information and Communications Technology

• Wu Youzheng

  Spoken Language Communication Group National Institute Of Information And Communications Technology

• Nakamura Satoshi

  Spoken Language Communication Group National Institute Of Information And Communications Technology (nict)

• Nakamura Satoshi

  Spoken Language Communication Group Knowledge Creating Communication Research Center National Institute Of Information And Communications Technology

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• Automatic Generation of Non-uniform and Context-Dependent HMMs Based on the Variational Bayesian Approach(Feature Extraction and Acoustic Medelings, Corpus-Based Speech Technologies)
• Automatic Generation of Non-uniform HMM Topologies Based on the MDL Criterion(Speech and Hearing)
• Iterative mapping function estimation and environment structure refinement in the online phase of the ESSEM approach (音声)
• An Unsupervised Model of Redundancy for Answer Validation

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