模式学习在QA系统中的有效实现

开放领域的问题回答(question answering)是自然语言处理领域中具有挑战性的研究方向.提出了一种基于模式学习实现问题回答的方法，核心思想是利用机器学习方法得到的答案模式获取问题答案.该方法优势在于：①模式学习完全自动化实现；②解决了目前普遍存在的模式约束性弱及答案缺乏语义类型限制等缺陷.在TREC测试集上的实验结果表明,它不但解决了简单模式所覆盖的问题集，同时也解决了需要较强约束性模式进行答案抽取的问题集，而后者的问题数目在TREC测试问题集中占约80%.

Effectively Implementing a Pattern Learning Method in the Question Answering System

Open domain question answering (QA) represents a challenge of natural language processing, aiming at returning exact answers in response to natural language questions. A novel pattern learning method for QA is developed. The key idea is to get answers using answer patterns learned from the Web. Although many other QA systems use the pattern based method, the method in this paper is implemented automatically and it can handle the problems other systems fail, such as the weakness of pattern restriction and so on. The experiment result on the TREC data indicates that the method is effective. It solves not only the questions relying on simple patterns, but also the questions that need complex patterns for answer extraction. The question number of the latter is about 80% in the question set of the TREC.