Calculating the similarity between words and sentences using a lexical database and corpus statistics

TL;DR

This paper introduces a new method for calculating semantic similarity between words and sentences by combining lexical database edge-based approaches with corpus statistics, achieving high correlation with human judgments.

Contribution

It presents a novel edge-based semantic similarity method that incorporates corpus statistics, outperforming existing models on benchmark datasets.

Findings

Achieved Pearson correlation of 0.8753 for word similarity.

Achieved Pearson correlation of 0.8794 for sentence similarity.

Outperformed other models on benchmark datasets.

Abstract

Calculating the semantic similarity between sentences is a long dealt problem in the area of natural language processing. The semantic analysis field has a crucial role to play in the research related to the text analytics. The semantic similarity differs as the domain of operation differs. In this paper, we present a methodology which deals with this issue by incorporating semantic similarity and corpus statistics. To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database. The methodology can be applied in a variety of domains. The methodology has been tested on both benchmark standards and mean human similarity dataset. When tested on these two datasets, it gives highest correlation value for both word and sentence similarity outperforming other similar models. For word similarity, we obtained Pearson correlation coefficient of 0.8753 and for sentence similarity, the correlation obtained is 0.8794.