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.
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页码：92 / 100
页数：8
相关论文
共 45 条
  • [1] Aamodt A., Et al., Requirements engineering, Requirements Engineering, 19, 1, pp. 35-46, (2013)
  • [2] Baccour L., Alimi A.M., John R.I., Some notes on fuzzy similarity measures and application to classification of shapes, recognition of Arabic sentences and mosaic, IAENG International Journal of Computer Science, 41, 2, pp. 81-90, (2014)
  • [3] Ben Aouicha M., Hadj Taieb M.A., Ben Hamadou A., SISR: system for integrating semantic relatedness and similarity measures, Soft Computing, 22, 6, pp. 1855-1879, (2018)
  • [4] Budanitsky A., Hirst G., Evaluating WordNet-based measures of lexical semantic relatedness, Computational Linguistics, 32, 1, pp. 13-47, (2006)
  • [5] Cai Y., Zhang Q., Lu W., Che X., A hybrid approach for measuring semantic similarity based on IC-weighted path distance in WordNet, Journal of Intelligent Information Systems, 51, 1, pp. 23-47, (2018)
  • [6] Chen R.C., Bau C.T., Yeh C.J., Merging domain ontologies based on the WordNet system and fuzzy formal concept analysis techniques, Applied Soft Computing Journal, 11, 2, pp. 1908-1923, (2011)
  • [7] Chen S., Measures of similarity between vague sets, Fuzzy sets and Systems, 74, 2, pp. 217-223, (1995)
  • [8] Duhan N., Mittal M., Opinion mining using ontological spam detection, 2017 International Conference on Infocom Technologies and Unmanned Systems (Trends and Future Directions) (ICTUS), pp. 557-562, (2017)
  • [9] Erk K., Vector space models of word meaning and phrase meaning: a survey, Linguistics and Language Compass, 6, 10, pp. 635-653, (2012)
  • [10] Finkelstein L., Et al., Placing search in context: the concept revisited, ACM Transactions on Information Systems, 20, 1, pp. 116-131, (2002)
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