Bridging the lexical chasm: statistical approaches to answer-finding
Bridging the lexical chasm: statistical approaches to answer-finding
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DOI：
10.1145/345508.345576
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发表时间：
2000-07
期刊：
影响因子：
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通讯作者：
A. Berger;R. Caruana;David A. Cohn;Dayne Freitag;Vibhu Mittal
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文献类型：
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作者：
A. Berger;R. Caruana;David A. Cohn;Dayne Freitag;Vibhu Mittal
本文研究机器是否可以在大量的候选人回答中自动学习查找的任务，该问题的答案包括检查回答的问题的集合并用统计模型来表征问题和答案之间的关系。为了学习这种关系，我们提出了两个数据来源：USENET FAQ文件和客户服务呼叫中心对话表明“答案”的任务与文件检索和传统的提问不同，提出的挑战与这些问题的核心目的是通过理论和经验研究发现的挑战。最适合答案问题。
This paper investigates whether a machine can automatically learn the task of finding, within a large collection of candidate responses, the answers to questions. The learning process consists of inspecting a collection of answered questions and characterizing the relation between question and answer with a statistical model. For the purpose of learning this relation, we propose two sources of data: Usenet FAQ documents and customer service call-center dialogues from a large retail company. We will show that the task of “answer-finding” differs from both document retrieval and tradition question-answering, presenting challenges different from those found in these problems. The central aim of this work is to discover, through theoretical and empirical investigation, those statistical techniques best suited to the answer-finding problem.