Semantic Textual Similarity using Machine Learning and Conceptual Relatedness

9 Pages Posted: 17 Apr 2020

See all articles by Shivam Varshney

Shivam Varshney

A.M.U. Aligarh U.P.

Priyanka Sharma

A.M.U. Aligarh U.P.

Hira Javed

A.M.U. Aligarh U.P.

Date Written: April 15, 2020

Abstract

Large amount of data is available in today’s world which can’t be stored in physical devices. This data contains huge amount of redundant information which could be grouped together and categorized. We present a system which gives the degree of equivalence between two statements i.e. Semantic Textual Similarity (STS). Given two textual fragments, the goal of the system is to determine their semantic similarity i.e. how much are they similar in terms of their meaning. Our system makes use of four different measures of text similarity: 1. Word n-gram overlap. 2. Character n-gram overlap. 3. Se-mantic overlap. 4. Conceptual overlap. Using these measures as features, it trains a sup-port vector regression model on SemEval STS data. Evaluation is done using the Pearson Correlation Coefficient.

Keywords: Semantic Textual Similarity, N-gram, Conceptual Similarity

Suggested Citation

Varshney, Shivam and Sharma, Priyanka and Javed, Hira, Semantic Textual Similarity using Machine Learning and Conceptual Relatedness (April 15, 2020). Proceedings of the International Conference on Advances in Electronics, Electrical & Computational Intelligence (ICAEEC) 2019, Available at SSRN: https://ssrn.com/abstract=3576366 or http://dx.doi.org/10.2139/ssrn.3576366

Shivam Varshney (Contact Author)

A.M.U. Aligarh U.P. ( email )

202002
India

Priyanka Sharma

A.M.U. Aligarh U.P. ( email )

202002
India

Hira Javed

A.M.U. Aligarh U.P. ( email )

202002
India

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