An Evaluative Baseline for Sentence-Level Semantic Division

Abstract

Abstract Semantic folding theory (SFT) is an emerging cognitive science theory that aims to explain how the human brain processes and organizes semantic information, and the distribution of text into semantic grids is key to SFT. We proposed the Sentence-Level Semantic Division Baseline with 100 grids (SSDB − 100), the only dataset we are currently aware of that performs a relevant validation of the sentence-level semantic folding theory algorithm, to test the validity of text distribution in semantic grids. In this article, we describe the construction of SSDB-100. Firstly, a semantic division questionnaire with broad coverage was generated by limiting the uncertainty range of the topics and the corpus. Then, through an expert survey, 11 human experts gave us feedback. Finally, we analyzed and processed the feedback, the average consistency index for the used feedback was 0.856 after eliminating the invalid feedback. With 100 semantic grids and 3215 sentences, the SSDB-100 is not only suitable for verifying semantic folding algorithms, but also for text clustering tasks.