Measuring interpretable semantic similarity of sentences using a multi chunk aligner

Issue title: Special Section: Intelligent and Fuzzy Systems applied to Language & Knowledge Engineering

Guest editors: David Pinto and Vivek Singh

Article type: Research Article

Authors: Majumder, Goutam^a | Pakray, Partha^{b; *} | Pinto, David^c

Affiliations: [a] School of Computer Science and Engineering, Lovely Professional University, Punjab, India | [b] Computer Science and Engineering, National Institute of Technology Silchar, Assam, India | [c] Benemérita Universidad Autónoma de Puebla, Puebla, Mexico

Correspondence: [*] Corresponding author. Partha Pakray, Computer Science and Engineering, National Institute of Technology Silchar, Assam, India. E-mail: [email protected].

Abstract:  This work focuses on bolstering the pre–existing Interpretable Semantic Textual Similarity (iSTS) method, that will enable a user to understand the behaviour of an artificial intelligent system. The proposed iSTS method explains the similarities and differences between a pair of sentences. The objective of the iSTS problem is to formalize the alignment between a pair of text segments and to label the relationship between the text fragments with a relation type and relatedness score. The overall objective of this work is to develop a 1:M multi chunk aligner for an iSTS method, which is trained on SemEval 2016 Task 2 dataset. The obtained result outperforms many state–of–art aligners, which were part of SemEval 2016 iSTS task.

Keywords: WordNet, interpretability, semantic semilarity, Natural Language Processing, cosine similarity

DOI: 10.3233/JIFS-179028

Journal: Journal of Intelligent & Fuzzy Systems, vol. 36, no. 5, pp. 4797-4808, 2019