{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T06:34:29Z","timestamp":1777703669778,"version":"3.51.4"},"reference-count":16,"publisher":"SAGE Publications","issue":"4","license":[{"start":{"date-parts":[[2017,3,29]],"date-time":"2017-03-29T00:00:00Z","timestamp":1490745600000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/journals.sagepub.com\/page\/policies\/text-and-data-mining-license"}],"content-domain":{"domain":["journals.sagepub.com"],"crossmark-restriction":true},"short-container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"published-print":{"date-parts":[[2017,3,29]]},"abstract":"<jats:p>Maintaining large collection of documents is an important problem in many areas of science and industry. Different analysis can be performed on large document collection with ease only if a short or reduced description can be obtained. Topic modeling offers a promising solution for this. Topic modeling is a method that learns about hidden themes from a large set of unorganized documents. Different approaches and alternatives are available for finding topics, such as Latent Dirichlet Allocation (LDA), neural networks, Latent Semantic Analysis (LSA), probabilistic LSA (pLSA), probabilistic LDA (pLDA). In topic models the topics inferred are based only on observing the term occurrence. However, the terms may not be semantically related in a manner that is relevant to the topic. Understanding the semantics can yield improved topics for representing the documents. The objective of this paper is to develop a semantically oriented probabilistic model based approach for generating topic representation from the document collection. From the modified topic model, we generate 2 matrices- a document-topic and a term-topic matrix. The reduced document-term matrix derived from these two matrices has 85% similarity with the original document-term matrix i.e. we get 85% similarity between the original document collection and the documents reconstructed from the above two matrices. Also, a classifier when applied to the document-topic matrix appended with the class label, shows an 80% improvement in F-measure score. The paper also uses the perplexity metric to find out the number of topics for a test set.<\/jats:p>","DOI":"10.3233\/jifs-169237","type":"journal-article","created":{"date-parts":[[2017,3,31]],"date-time":"2017-03-31T18:22:01Z","timestamp":1490984521000},"page":"2941-2951","update-policy":"https:\/\/doi.org\/10.1177\/sage-journals-update-policy","source":"Crossref","is-referenced-by-count":18,"title":["Semantics-based topic inter-relationship extraction"],"prefix":"10.1177","volume":"32","author":[{"given":"Remya R.K.","family":"Menon","sequence":"first","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Engineering, AmritapuriAmrita Vishwa Vidyapeetham, Amrita University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Deepthy","family":"Joseph","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Engineering, AmritapuriAmrita Vishwa Vidyapeetham, Amrita University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"M.R.","family":"Kaimal","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, Amrita School of Engineering, AmritapuriAmrita Vishwa Vidyapeetham, Amrita University, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","published-online":{"date-parts":[[2017,3,29]]},"reference":[{"key":"e_1_3_3_2_2","article-title":"Proceedings of ICCM","author":"Niekler A.","year":"2012","unstructured":"NieklerA. and J\u00e3d\u2019hnichenP., Matching Result Of Latent Dirichlet Allocation for Text, Proceedings of ICCM, 2012.","journal-title":"Matching Result Of Latent Dirichlet Allocation for Text"},{"key":"e_1_3_3_3_2","first-page":"993","article-title":"Latent dirichlet allocation","volume":"3","author":"Blei D.M.","year":"2003","unstructured":"BleiD.M., NgA.Y. and JordanM.I., Latent dirichlet allocation, Journal of Machine Learning Research3 (2003), 993\u20131022.","journal-title":"Journal of Machine Learning Research"},{"key":"e_1_3_3_4_2","article-title":"Supervised topic model","volume":"20","author":"Blei D.M.","year":"2007","unstructured":"BleiD.M. and McAuliffeJ.D., Supervised topic model, Advances in Neural Information Processing Systems20 (2007).","journal-title":"Advances in Neural Information Processing Systems"},{"key":"e_1_3_3_5_2","doi-asserted-by":"publisher","DOI":"10.1145\/2133806.2133826"},{"key":"e_1_3_3_6_2","article-title":"Information retrieval using a singular value decomposition model of latent semantic structure","author":"Furnas G.W.","year":"1988","unstructured":"FurnasG.W., DeerwesterS., DumaisS.T., LandauerT.K., HarshmanR.A., StreeterL.A. and LochbaumK.E., Information retrieval using a singular value decomposition model of latent semantic structure, Proceedings of the 11th International ACM SIGIR Conference on Research and Development in Information Retrieval, 1988.","journal-title":"Proceedings of the 11th International ACM SIGIR Conference on Research and Development in Information Retrieval"},{"key":"e_1_3_3_7_2","article-title":"Evaluation Methods for Topic Models","author":"Wallach H.M.","year":"2009","unstructured":"WallachH.M., MurrayI., SalakhutdinovR. and MimnoD., Evaluation Methods for Topic Models, Proceedings of the 26th International Conference on Machine Learning (ICML), 2009.","journal-title":"Proceedings of the 26th International Conference on Machine Learning (ICML)"},{"key":"e_1_3_3_8_2","article-title":"Multi-class text categorization based on LDA and SVM","author":"Li K.","year":"2011","unstructured":"LiK., XieJ., SunX., MaY. and BaiH., Multi-class text categorization based on LDA and SVM, Procedia Engineering in Advanced in Control Engineering and Information Science, 2011.","journal-title":"Procedia Engineering in Advanced in Control Engineering and Information Science"},{"key":"e_1_3_3_9_2","doi-asserted-by":"publisher","DOI":"10.1613\/jair.2934"},{"key":"e_1_3_3_10_2","first-page":"387","article-title":"Document Classification with Hierarchically Structured Dictionaries","author":"Menon R.R.K.","year":"2015","unstructured":"MenonR.R.K. and AswathiP., Document Classification with Hierarchically Structured Dictionaries, Volume 385 of the Series Advances in Intelligent Systems and Computing, 2015, pp. 387\u2013397.","journal-title":"Volume 385 of the Series Advances in Intelligent Systems and Computing"},{"key":"e_1_3_3_11_2","article-title":"Modeling Word Burstiness Using the Dirichlet Distribution","author":"Madsen R.E.","year":"2005","unstructured":"MadsenR.E., KauchakD. and ElkanC., Modeling Word Burstiness Using the Dirichlet Distribution, Proceeding ICML, Proceedings of the 22nd international conference on Machine Learning, 2005.","journal-title":"Proceeding ICML, Proceedings of the 22nd international conference on Machine Learning"},{"key":"e_1_3_3_12_2","doi-asserted-by":"publisher","DOI":"10.1002\/(SICI)1097-4571(199009)41:6<391::AID-ASI1>3.0.CO;2-9"},{"issue":"4","key":"e_1_3_3_13_2","article-title":"An algorithm for pronominal anaphora resolution","volume":"20","author":"Lappin S.","year":"1994","unstructured":"LappinS. and LeassH.J., An algorithm for pronominal anaphora resolution, Journal Computational Linguistics Archive20(4) (1994).","journal-title":"Journal Computational Linguistics Archive"},{"key":"e_1_3_3_14_2","article-title":"Probabilistic Latent Semantic Indexing","author":"Hofmann T.","year":"1999","unstructured":"HofmannT., Probabilistic Latent Semantic Indexing, Proceedings of the Twenty-Second Annual International SIGIR Conference, 1999.","journal-title":"Proceedings of the Twenty-Second Annual International SIGIR Conference"},{"key":"e_1_3_3_15_2","article-title":"Sparse Latent Semantic Analysis","author":"Chen X.","unstructured":"ChenX., QiY., LinQ. and CarbonellJ.G., Sparse Latent Semantic Analysis, Proceedings of the 2011 SIAM International Conference on Data Mining.","journal-title":"Proceedings of the 2011 SIAM International Conference on Data Mining"},{"key":"e_1_3_3_16_2","article-title":"Reading Tea Leaves: How Humans Interpret Topic Models","author":"Chang J.","year":"2009","unstructured":"ChangJ., Boyd-GraberJ., WangC., GerrishS. and BleiD.M., Reading Tea Leaves: How Humans Interpret Topic Models, Neural Information Processing Systems, 2009.","journal-title":"Neural Information Processing Systems"},{"key":"e_1_3_3_17_2","unstructured":"ManningC.D. RaghavanP. and SchutzeH. \u201cBoolean Retrieval\u201d in Introduction to Information Retrieval Cambridge University Press 2012 pp. 3\u201313."}],"container-title":["Journal of Intelligent &amp; Fuzzy Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169237","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/full-xml\/10.3233\/JIFS-169237","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/journals.sagepub.com\/doi\/pdf\/10.3233\/JIFS-169237","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,29]],"date-time":"2026-04-29T09:39:17Z","timestamp":1777455557000},"score":1,"resource":{"primary":{"URL":"https:\/\/journals.sagepub.com\/doi\/10.3233\/JIFS-169237"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2017,3,29]]},"references-count":16,"journal-issue":{"issue":"4","published-print":{"date-parts":[[2017,3,29]]}},"alternative-id":["10.3233\/JIFS-169237"],"URL":"https:\/\/doi.org\/10.3233\/jifs-169237","relation":{},"ISSN":["1064-1246","1875-8967"],"issn-type":[{"value":"1064-1246","type":"print"},{"value":"1875-8967","type":"electronic"}],"subject":[],"published":{"date-parts":[[2017,3,29]]}}}