关键词: 语义分析, 神经网络, 组合语义, 文本相似度, 词义表示

Abstract: In order to improve the accuracy for semantic similarity of short texts, this paper introduces a new method based on neural language model and compositionality in distributional semantics. Firstly the neural language model learns word representations with the local context and the global context, and better captures the semantics of words. And then it uses a dependency parse visualization tool, to get the dependence tree of the text, then using a semantic compositionality model to get the text semantic representation. According to the semantic representation, it uses VSM to judge the semantic relevance between the texts. The experimental results show this method can improve the accuracy for semantic similarity of short texts.

Key words: semantic analysis, neural network, semantic compositionality, text similarity, word representation