基于本体和语义文法的上下文相关问答

在问答系统中,用户的提问通常不是孤立的,而是使用连续的多个相关的问题来获取信息,用户在与这样的系统进行交互时,才会感觉更自然。在已构建的非上下文相关问答系统的基础上,该文提出了一种可以处理上下文相关问题的方法并开发了系统OSG-IQAs。方法首先识别当前问题是否是一个从问题(follow-up),并判别其与前面问题的具体的相关类别,然后根据相关类别,利用话语结构中的信息对当前的follow-up问题进行重构,并提交到非上下文相关问答系统中。最后,将方法在两个不同规模的领域进行测试,并与相关系统或方法进行比较,测试结果表明,该方法具有较好的可扩展性。在总体测试中,该方法比基线系统获得了更好地效果,同时利用手工将所有上下文相关问题进行上下文消解,系统与此也进行了比较,并获得了相近的性能。

Interactive Question Answering Based on Ontology and Semantic Grammar

In QA system, the user queries are usually not isolated, but correlated. This paper proposes an ontology and semantic grammar based method for interactive question answering, and we developes a QA system called OSG-IQAs based on an existing non-contextual question answering system. We first propose a discourse structure to maintain semantic information (i.e., the understanding result) of questions, and then use an approach to recognizing the specific type of relevancy between the previous question and follow-up question. We then propose an algorithm which fuses different contextual information (recorded in discourse structure) into the current, follow-up question according to the specific relevancy type. Lastly, the transformed question is resubmitted to the non-contextual question answering system. We finally evaluate the proposed method on two real contextual QA data sets from two areas of different scales. The results show that the proposed method has better scalability; we achieved an overall performance better than a baseline system and almost the same performance as another comparison system whose contextual phenomena are manually resolved.