融合事件信息的中文问答系统问题语义表征

复杂类问题理解是中文问答系统研究的难点,基于组块的问句分析方法将整个问句转化为若干组块,降低了问句分析的难度和复杂性。针对以含有事件(动作)信息的复杂类问题,提出基于语义组块的中文问答系统问题语义表征模型,采用语义组块的思想将问题的语义成分定义为疑问焦点块、问题主题块和问题事件块三个语义组块,对问句中的事件语义信息,建立了问题事件语义结构,将一个问句表征为一个基于语义组块的问题语义表征结构,用于问答系统的问题理解。通过序列标注学习方法实现问题语义表征中语义组块自动标注。实验结果表明:问题语义组块标注效果较好,问题语义表征模型获取了问题的关键语义信息,为语义层面上的问题理解提供基础。

Event Information Enhanced Question Semantic Representation for Chinese Question Answering System

Question understanding of complex questions is a challenging issue in question answering system. For complex questions containing events (actions) information, this paper presents a question semantic representation (QSR) model based on semantic chunk. The semantic components of a complex question are labeled abstractly as the question focus, the question topic and the question event. A Semantic Structure of Question Event is then created to represent the semantic information of question event, including the question focus chunk, the question topic chunk and the question event chunk. To map the interrogative sentence into this question semantic representation, the Conditional Random Fields model is adopted for automatic semantic labeling of question semantic representation. The results show that automatic semantic labeling gains better performance.