A Method for Perception and Assessment of Semantic Textual Similarities in English

Abstract

This research proposes a method for the detection of semantic similarities in text snippets; the method achieves an unsupervised extraction and comparison of semantic information by mimicking skills for the identification of clauses and possible verb conjugations, the selection of the most accurate organization of the parts of speech, and similarity analysis by a direct comparison on the parts of speech from a pair of text snippets. The method for the extraction of the parts of speech in each text exploits a knowledge base structured as a dictionary and a thesaurus to identify the possible labels of each word and its synonyms. The method consists of the processes of perception, debiasing, reasoning and assessment. The perception module decomposes the text into blocks of information focused on the elicitation of the parts of speech. The debiasing module reorganizes the blocks of information due to the biases that may be produced in the previous perception. The reasoning module finds the similarities between blocks from two texts through analyses of similarities on synonymy, morphological properties, and the relative position of similar concepts within the texts. The assessment generates a judgement on the output produced by the reasoning as the averaged similarity assessment obtained from the parts of speech similarities of blocks. The proposed method is implemented on an English language version to exploit a knowledge base in English for the extraction of the similarities and differences of texts. The system implements a set of syntactic and logical rules that enable the autonomous reasoning that uses a knowledge base regardless of the concepts and knowledge domains of the latter. A system developed with the proposed method is tested on the “test” dataset used on the SemEval 2017 competition on seven knowledge bases compiled from six dictionaries and two thesauruses. The results indicate that the performance of the method increases as the degree of completeness of concepts and their relations increase, and the Pearson correlation for the most accurate knowledge base is 77%.

Keywords

• knowledge bases
• unsupervised reasoning
• concept elicitation
• computational epistemic skills
• semantic text similarity