A Novel Discrimination Structure for Assessing Text Semantic Similarity

Abstract

Discrimination of semantic textual similarity refers to comparing the similarity between two or more entities (including words, short texts and documents) through certain strategies to obtain a specific quantitative similarity value. Traditional research put more experience into the similarity calculation of the original text content, using the matching degree or distance of characters or words as the yardstick to judge whether the text pairs are similar. However, there are still some problems to be solved in the following aspects: the key points of sentence meaning and word semantics, which play important role in the semantic expression of natural language, are not well integrated into the similarity discrimination, and the interactive features between texts are not fully utilized. To solve the above problems, this paper proposes a novel discrimination structure based on the Siamese Network model and the idea of text matching. In this structure, we introduce sentence meaning key information and word semantic information to realize the extraction of word interaction feature information, and then we realize the text vector representation by using Siamese BiLSTM. The experimental results showed that the accuracy of the proposed model is higher than that of the basic models.