A joint FrameNet and element focusing Sentence-BERT method of sentence similarity computation

Abstract

As one of the fundamental research areas of natural language processing, sentence similarity computation attracts researchers’ attention. Considering two single independent sentences, it is difficult to measure the similarity between them without sufficient context information. To solve this issue, we propose a joint FrameNet and element focusing Sentence-BERT method of sentence similarity computation (FEFS3C). Considering the actual meaning of sentences, we adopt the frame semantics theory and adapt FrameNet in FEFS3C. Moreover, focusing on critical information conveyed in sentences, FEFS3C takes the superiority of deep learning technologies and proposes a new sentence representation model element focusing Sentence-BERT (EF-SBERT) which improves traditional sentence representations. Two primary considerations of sentences in FEFS3C “sentence meaning” and “critical sentence information” aim to better utilize the influence of sentences context. To evaluate the performance of FEFS3C, we carried out experiments on the standard test set “STS-B”. Results show that FEFS3C has obtained better Spearman correlation compared with traditional methods.