Learning foci for Question Answering over Topic Maps
Learning foci for Question Answering over Topic Maps
复制标题
通过主题图进行问答的学习重点
DOI：
10.3115/1667583.1667684
复制
发表时间：
2009
期刊：
影响因子：
--
通讯作者：
R. Pinchuk
中科院分区：
文献类型：
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作者：
Alexander Mikhailian;T. Dalmas;R. Pinchuk
作为问题焦点的变体，本文引入了提问点和期望回答类型的概念。它们对于半结构化数据上的QA特别重要，如主题地图、OWL或定制的XML格式所表示的。我们描述了一种从主题地图上的问答系统提出的问题中识别问题焦点的方法，通过提取提问点并在必要时回退到预期的答案类型来实现。我们使用已知的机器学习技术来提取期望答案类型，并实现了一种新的问点提取方法。我们还提供了一个数学模型来预测系统的性能。
This paper introduces the concepts of asking point and expected answer type as variations of the question focus. They are of particular importance for QA over semistructured data, as represented by Topic Maps, OWL or custom XML formats. We describe an approach to the identification of the question focus from questions asked to a Question Answering system over Topic Maps by extracting the asking point and falling back to the expected answer type when necessary. We use known machine learning techniques for expected answer type extraction and we implement a novel approach to the asking point extraction. We also provide a mathematical model to predict the performance of the system.