Efficient Computational linguistics Framework for Concept Drift Detection
A. Uma Maheswari1, N. Revathy2
1Ms. A. Uma Maheswari, Ph. D Research Scholar, Department of Computer Science, Hindusthan College of Arts and Science, Coimbatore, India.
2Dr. N. Revathy, Professor, PG and Research Department of Computer Applications, Hindusthan College of Arts and Science, Coimbatore, India.
Manuscript received on 23 August 2019. | Revised Manuscript received on 07 September 2019. | Manuscript published on 30 September 2019. | PP: 4305-4310 | Volume-8 Issue-11, September 2019. | Retrieval Number: K14570981119/19©BEIESP | DOI: 10.35940/ijitee.K1457.0981119
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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: Semantic drift is a common problem in iterative information extraction. Unsupervised bagging and incorporated distributional similarity is used to reduce the difficulty of semantic drift in iterative bootstrapping algorithms, particularly when extracting large semantic lexicons. Compared to previous approaches which usually incur substantial loss in recall, DP-based cleaning method can effectively clean a large proportion of semantic drift errors while keeping a high recall.
Keywords: Drifting Points, Deep Neural Network, Information Retrieval, Lexical Semantics, computational phonetics or word elucidation (WSD
Scope of the Article: Neural Information Processing