Challenging the Boundaries of Unsupervised Learning for Semantic Similarity

Abstract

The semantic analysis field has a crucial role to play in the research related to text analytics. Calculating the semantic similarity between sentences is a long-standing problem in the area of natural language processing, and it differs significantly as the domain of operation differs. In this paper, we present a methodology that can be applied across multiple domains by incorporating corpora-based statistics into a standardized semantic similarity algorithm. To calculate the semantic similarity between words and sentences, the proposed method follows an edge-based approach using a lexical database. When tested on both benchmark standards and mean human similarity dataset, the methodology achieves a high correlation value for both word ( $r=0.8753$ ) and sentence similarity ( $r=0.8793$ ) concerning Rubenstein and Goodenough standard and the SICK dataset ( $r=0.8324 $ 1 ) outperforming other unsupervised models. 1 Eliminating the outliers which constitutes to 3.75% of 4927 statement pairs