Term Categorization Using Latent Semantic Analysis for Intelligent Query Processing
K. Selvi1, P. Shobharani2, M. L. Aishwarya3, M. Rajkuumar4
1K. Selvi, Assistant Professor, Department of Computer Science and Engineering, R.M.K. Engineering College, (Tamil Nadu), India.
2P. Shobharani, Associate Professor, Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai (Tamil Nadu), India.
3M. L. Aishwarya, Assistant Professor, Department of Information Technology, R.M.K. Engineering College, (Tamil Nadu), India.
4M. Rajkuumar, Associate Professor, Department of Computer Science and Engineering, R.M.D. Engineering College, Chennai (Tamil Nadu), India.
Manuscript received on 25 November 2019 | Revised Manuscript received on 06 December 2019 | Manuscript Published on 14 December 2019 | PP: 317-322 | Volume-9 Issue-1S November 2019 | Retrieval Number: A10651191S19/2019©BEIESP | DOI: 10.35940/ijitee.A1065.1191S19
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: With the rapid improvement in the field of social networks, a huge amount of small size texts are generated within a fraction of a second. Understanding and categorizing these texts for effective query processing is considered as one of the vital defy in the field of Natural Language Processing. The objective is to retrieve only relevant documents by categorizing the short texts. In the proposed method, terms are categorized by means of Latent Semantic Analysis (LSA). Our novel method focuses on applying the semantic enrichment for term categorization with the target of augmenting the unstructured data items for achieving faster and intelligent query processing in the big data environment. Therefore, retrieval of documents can be made effective with the flexibility of query term mapping.
Keywords: Machine Learning; Natural Language Processing; Knowledge Engineering; Term Similarity; Latent Semantic Analysis; Text Categorization; Query Processing.
Scope of the Article: Neural Information Processing