Learning surface text patterns for a Question Answering System
Learning surface text patterns for a Question Answering System
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DOI：
10.3115/1073083.1073092
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发表时间：
2002-07
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通讯作者：
Deepak Ravichandran;E. Hovy
中科院分区：
文献类型：
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作者：
Deepak Ravichandran;E. Hovy
在本文中，我们探讨了开放域问答系统的表层文本模式的力量。为了获得一组最佳的模式，我们已经开发了一种方法来自动学习这种模式。通过向阿尔塔维斯塔提供每种问题类型的一些手工制作的例子，在自举过程中从互联网构建了一个标记的语料库。然后从返回的文档中自动提取模式并进行标准化。我们计算每个模式的精确度以及每个问题类型的平均精确度。然后，这些模式被应用于寻找新问题的答案。使用TREC-10问题集，我们报告了两种情况下的结果：从TREC-10语料库和网络确定的答案。
In this paper we explore the power of surface text patterns for open-domain question answering systems. In order to obtain an optimal set of patterns, we have developed a method for learning such patterns automatically. A tagged corpus is built from the Internet in a bootstrapping process by providing a few hand-crafted examples of each question type to Altavista. Patterns are then automatically extracted from the returned documents and standardized. We calculate the precision of each pattern, and the average precision for each question type. These patterns are then applied to find answers to new questions. Using the TREC-10 question set, we report results for two cases: answers determined from the TREC-10 corpus and from the web.