Question-answer matching method fusing deep representation and interaction model

The invention discloses a question-answer matching method fusing deep representation and an interaction model. The method comprises the following steps: firstly, pre-training word vectors and character vectors of a text pair consisting of spoken questions input by a user and standard questions in a knowledge base; and then fusing the fusion depth representation and the interaction model in a loosecombination or tight combination mode, and performing question and answer matching on spoken questions input by the user by using the fused model. The universality of the service can be enhanced, therecognizable spoken language category can be expanded, the answer matching accuracy can be improved, and accurate semantic matching of wide semantic recognition can be realized. 本发明公开一种融合深度表示与交互模型的问答匹配方法，本方法首先对用户输入的口语化问句和知识库中的标准问题组成的文本对进行词向量和字符向量的预训练，然后将融合深度表示与交互模型通过松组合或者紧组合的方式进行融合，使用融合后的模型对用户输入的口语化问句进行问答匹配。本方法可以增强服务的通用性，扩大可识别的口语化范畴，提高答案匹配的准确率，实现广泛的语义识别的精确的语义匹配。