Measuring the short text similarity based on semantic and syntactic information

Determining the similarity between short texts plays an important role in natural language processing applications such as search, query suggestion and automatic summary, which has attracted widespread attention. Unlike traditional long texts, short texts present the characteristics of short length, weak signal, and high ambiguity. Researchers have proposed many methods, from simple vector space models to more sophisticated distributed semantics. However, these methods only consider the literal meaning of words, ignoring the impact of word ambiguity and the semantic information contained in the structure of the short text. Additionally, words on their own are often insufficient for expressing semantics, as many terms are composed of multiple words. In this paper, we propose a method based on semantic and syntactic information for short text similarity calculations by using knowledge and corpora to express the meaning of the term to solve polysemy, and using a constituency parse tree to capture the syntactic structure of short texts. Additionally, the proposed method uses terms as semantic units. Experimental results on ground-truth datasets demonstrate that the proposed method outperforms baseline methods. (C) 2020 Elsevier B.V. All rights reserved.

Automated summary

The study highlights the importance of determining the similarity between short texts in natural language processing applications and proposes a new method based on semantic and syntactic information to enhance the accuracy of short text similarity calculations by addressing polysemy and capturing syntactic structure.