Computing semantic relatedness using latent semantic analysis and fuzzy formal concept analysis

被引：2

作者：

Jain S. ^{[1
]}

Seeja K.R. ^{[1
]}

Jindal R. ^{[2
]}

机构：

[1] Department of Computer Science and Engineering, Indira Gandhi Delhi Technical University for Women, Delhi

[2] Department of Computer Science and Engineering, Delhi Technological University, Delhi

来源：

Jain, Shivani (shivanijain13@gmail.com) | 1600年 / Inderscience Publishers卷 / 13期

关键词：

FFCA; Fuzzy formal concept analysis; Fuzzy set similarity measure; Latent semantic analysis; LSA; Semantic relatedness; Semantic similarity;

D O I：

10.1504/IJRIS.2021.114635

中图分类号：

TP3 [计算技术、计算机技术];

学科分类号：

0812 ;

摘要：

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. Copyright © 2021 Inderscience Enterprises Ltd.

引用

页码：92 / 100

页数：8

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