基于模式学习的中文问答系统答案抽取方法

答案抽取是中文问答系统的关键,而通常答案是借助于问题的答案句子模式抽取得到,由于答案句子模式是语言专家根据语言规则提炼获得,因此非常依赖于专家经验。针对这一局限性,提出了一种利用模式学习来获得中文答案句子模式的方法,该方法利用搜索引擎从互连网上检索相关问题文本,人工提取包含答案的句子段,并标注问题类型及答案,形成各种问题类型的问答训练语料。通过统计学习,提取候选答案句子模式,计算候选句子模式权重,并根据权重获得相应问题类型的答案句子模式。基于事实的问题答案抽取结果表明,提出的基于模式学习的方法有很好的效果,实验答案提取准确率值达到了0.28,学习方法获得的模式基本上覆盖了常规答案句子模式。

Answer extraction scheme for Chinese question answering system based on pattern learning

Answer extraction is the key of the Chinese questioning system.Normally answer extraction mainly depends on the pattern of the answer sentence.Since the pattern of the answer sentence is obtained by experts based on the language rules,so it strongly relies on the experts' language knowledge. To overcome this limit,a scheme is proposed to gain the pattern of the answer sentence by pattern learning.The scheme takes the advantage of searching engine to retrieve related documents of question.From these documents the sentences that include the answers are extracted.Then the types of the questions and answers are marked to form question-answer training corpus to the questions of different types.Then by statistic learning method,the candidates of sentence patterns are abstracted and the weights of the patterns are calculated.Thus,based on the weights the patterns of the answer sentences to the questions of different types are obtained.Answer extraction result of the factoid question shows that the experimental MRAR is up to 0.28,which indicates the effectiveness of the proposed pattern learning scheme.The patterns gained by pattern learning cover the normal answer sentences.