融合语言知识与深度学习的文本蕴含识别

文本蕴含技术在自然语言处理中得到了广泛应用，但存在词对推理能力差的问题（例如，句对中出现反义词对无法判断反义关系等）。重点研究了词对知识向量的获取问题，包括融合多特征及有监督的词对关系向量获取、采用TransR的词对关系表示获取、反义词向量表示获取等三种方法，并将知识向量引入到文本蕴含识别模型中的词对齐和注意力机制部分。有关实验表明，上述方法相比经典模型有了较大的提升。

Text Entailment Recognition Based on Integration of Language Knowledge and Deep Learning

Text entailment technology has been widely used in natural language processing, but there are some problems such as the poor reasoning ability of word pairs （for example, there is the antonym pairs in sentence pairs, but can’t judge the antonym relationship, etc.）. This paper focuses on the acquisition of knowledge vector from words, including acquire the word pairs relation vector by integration of multi feature and supervised method, acquire word pairs relation expression using TransR tools, and acquire antonym vector expression. Knowledge vector is introduced into the part of word alignment and attention mechanism in text entailment recognition model. The experimental results show that the proposed method is better than the classical model.