为了解决问答处理系统中的语义模糊问题,提高问答处理的性能,研究人员尝试采用概念作为系统处理的对象,而不再是语言表层符号,然而,在引入概念进行处理的同时引来了一些新的问题,如概念的抽取、概念关联计算以及特定于问答系统的问题理解、问题求解、答案生成等问题。在概念抽取、概念关联计算方面,已有一些比较成功的算法。本文将在此基础上,针对实现这样一个问答处理系统所存在的一些未涉及的核心问题进行一个探讨,同时提出解决以上问题的方法。实验及实际应用表明基于所提出算法的概念问答系统具有较强的性能,系统总体自动处理准确率将近达到40%。在实际应用中也表现出较高的应用价值。

Concept-Based Question Answering (QA) is a brand new research topic which takes concepts, instead of the lexical terms, as the processing object. Concepts, as a formalized meaning, can well help to resolve the word sense ambiguities. However, using concepts brings some new problems, such as the concept extracting; the semantic relativity calculation for concept as well as the QA-specialized issues such as how to comprehend the query; how to search the answers and how to generate the nature language answers. Most of them, especially the QA-specialized issues, have not been addressed. In this paper, we discuss these key issues for carrying out a concept-based QA system. Some algorithms will also be proposed in order to solve the problems. The experiments indicate that the concept-based QA system powered by the proposed algorithms performs very well. The precision of the system reaches almost 40%. The actual application also indicates these algorithms contribute a lot to a commercial concept-based QA setting.