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
收藏
页码：92 / 100
页数：8
相关论文
共 50 条
  • [31] COMPARISON OF LATENT SEMANTIC ANALYSIS AND PROBABILISTIC LATENT SEMANTIC ANALYSIS FOR DOCUMENTS CLUSTERING
    Kuta, Marcin
    Kitowski, Jacek
    COMPUTING AND INFORMATICS, 2014, 33 (03) : 652 - 666
  • [32] Semantic relation modeling using formal concept analysis in Russian lexical databases
    Emelyanov, GM
    Stepanova, NA
    Proceedings of the Second IASTED International Multi-Conference on Automation, Control, and Information Technology - Software Engineering, 2005, : 9 - 12
  • [33] Semantic similarity measures for formal concept analysis using linked data and WordNet
    Yuncheng Jiang
    Mingxuan Yang
    Rong Qu
    Multimedia Tools and Applications, 2019, 78 : 19807 - 19837
  • [34] Using Formal concept analysis and Rough Set Constructing the System of Semantic web
    Xu, Hong-sheng
    Zhang, Rui-ling
    INFORMATION TECHNOLOGY FOR MANUFACTURING SYSTEMS II, PTS 1-3, 2011, 58-60 : 1664 - 1670
  • [35] Mining for Reengineering: An Application to Semantic Wikis Using Formal and Relational Concept Analysis
    Shi, Lian
    Toussaint, Yannick
    Napoli, Amedeo
    Blansche, Alexandre
    SEMANTIC WEB: RESEARCH AND APPLICATIONS, PT II, 2011, 6644 : 421 - 435
  • [36] Semantic similarity measures for formal concept analysis using linked data and WordNet
    Jiang, Yuncheng
    Yang, Mingxuan
    Qu, Rong
    MULTIMEDIA TOOLS AND APPLICATIONS, 2019, 78 (14) : 19807 - 19837
  • [37] Semantic coherence in psychometric schizotypy: An investigation using Latent Semantic Analysis
    Marggraf, Matthew P.
    Cohen, Alex S.
    Davis, Beshaun J.
    DeCrescenzo, Paula
    Bair, Natasha
    Minor, Kyle S.
    PSYCHIATRY RESEARCH, 2018, 259 : 63 - 67
  • [38] Using the structure of a conceptual network in computing semantic relatedness
    Gurevych, I
    NATURAL LANGUAGE PROCESSING - IJCNLP 2005, PROCEEDINGS, 2005, 3651 : 767 - 778
  • [39] Formal concept analysis for an e-learning semantic web
    Beydoun, Ghassan
    EXPERT SYSTEMS WITH APPLICATIONS, 2009, 36 (08) : 10952 - 10961
  • [40] An semantic rank for web crawler based on formal concept analysis
    Du, Yajun
    Li, Xinchun
    PROCEEDINGS OF THE INTERNATIONAL CONFERENCE ON INTELLIGENT SYSTEMS AND KNOWLEDGE ENGINEERING (ISKE 2007), 2007,
12345