Title: Computing semantic relatedness using latent semantic analysis and fuzzy formal concept analysis
Authors: Shivani Jain; K.R. Seeja; Rajni Jindal
Addresses: Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, Delhi, India ' Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, Delhi, India ' Department of Computer Science and Engineering, Delhi Technological University, Delhi, India
Abstract: Measuring semantic similarity/semantic relatedness is an important task in computational linguistic, natural language processing and ontology creation. In this paper, a new hybrid method using LSA and FFCA is proposed for computing the semantic-relatedness. Latent semantic analysis (LSA) is used to extract the attributes of the concepts and these attributes are further mapped to FFCA to compute semantic relatedness. The latent semantic analysis is used for finding the neighbouring words or attributes and their correlation value. The concepts and their attributes are mapped to FCA table and then to FFCA table by using the correlation value as membership. A fuzzy similarity measure is then used to compute the semantic relatedness between these concepts/words. The proposed method is evaluated on word similarity bench mark dataset WS-353 and found an accuracy of 0.85.
Keywords: semantic relatedness; semantic similarity; latent semantic analysis; LSA; fuzzy formal concept analysis; FFCA; fuzzy set similarity measure.
International Journal of Reasoning-based Intelligent Systems, 2021 Vol.13 No.2, pp.92 - 100
Received: 19 Jul 2019
Accepted: 08 Dec 2019
Published online: 30 Mar 2021 *