基于卷积神经网络的自动问答

The auto-question answering system based on convolution neural network

自动问答是自然语言处理领域中的一个研究热点,自动问答系统能够用简短、精确的答案直接回答用户提出的问题,给用户提供更加精确的信息服务.自动问答系统中需解决两个关键问题:一是实现自然语言问句及答案的语义表示,另一个是实现问句及答案间的语义匹配.卷积神经网络是一种经典的深层网络结构,近年来卷积神经网络在自然语言处理领域表现出强大的语言表示能力,被广泛应用于自动问答领域中.本文对基于卷积神经网络的自动问答技术进行了梳理和总结,从语义表示和语义匹配两个主要角度分别对面向知识库和面向文本的问答技术进行了归纳,并指出了当前的研究难点.

The question-answering is a hot research field in natural language processing,which can give users concise and precise answer to the question presented in natural language and provide the users with more accurate information service. There are two key questions to be solved in the question answering system： one is to realize the semantic representation of natural language question and answer, and the other is to realize the semantic matching learning between question and answer. Convolution neural network is a classic deep network structure which has a strong ability to express semantics in the field of natural language processing in recent years, and is widely used in the field of automatic question and answer. This paper reviews some techniques in the question answering system that is based on the convolution neural network, the paper focuses on the knowledge-based and the text-oriented QA techniques from the two main perspectives of semantic representation and semantic matching, and indicates the current research difficulties.