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首页> 外文会议>International Conference on Computational Linguistics and Intelligent Text Processing(CICLing 2006); 20060219-25; Mexico City(MX) >Automatic Acquisition of Semantic-Based Question Reformulations for Question Answering
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Automatic Acquisition of Semantic-Based Question Reformulations for Question Answering

机译：自动获取基于语义的问题解答公式

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摘要

In this paper, we present a method for the automatic acquisition of semantic-based reformulations from natural language questions. Our goal is to find useful and generic reformulation patterns, which can be used in our question answering system to find better candidate answers. We used 1343 examples of different types of questions and their corresponding answers from the TREC-8, TREC-9 and TREC-10 collection as training set. The system automatically extracts patterns from sentences retrieved from the Web based on syntactic tags and the semantic relations holding between the main arguments of the question and answer as defined in WordNet. Each extracted pattern is then assigned a weight according to its length, the distance between keywords, the answer sub-phrase score, and the level of semantic similarity between the extracted sentence and the question. The system differs from most other reformulation learning systems in its emphasis on semantic features. To evaluate the generated patterns, we used our own Web QA system and compared its results with manually created patterns and automatically generated ones. The evaluation on about 500 questions from TREC-11 shows comparable results in precision and MRR scores. Hence, no loss of quality was experienced, but no manual work is now necessary.
机译：在本文中，我们提出了一种从自然语言问题中自动获取基于语义的表述的方法。我们的目标是找到有用的通用重新定义模式，可以在我们的问题回答系统中使用它来找到更好的候选答案。我们从TREC-8，TREC-9和TREC-10集合中使用了1343个不同类型问题的示例及其对应的答案作为训练集。该系统会根据句法标签以及WordNet中定义的问题和答案主要论点之间的语义关系，从网上检索的句子中自动提取模式。然后，根据提取的模式的长度，关键字之间的距离，答案子短语分数以及提取的句子和问题之间的语义相似度，为每个提取的模式分配权重。该系统与其他大多数重构学习系统的不同之处在于对语义特征的强调。为了评估生成的模式，我们使用了自己的Web质量检查系统，并将其结果与手动创建的模式和自动生成的模式进行了比较。对TREC-11中约500个问题的评估显示出在准确性和MRR得分上可比的结果。因此，没有质量损失的经历，但是现在不需要手工工作。

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