Open AccessComprehensive Review on Effectual Information Retrieval of Semantic Drift using Deep Neural Network
Author(s) -
A. Uma Maheswari,
N. Revathy
Publication year - 2019
Publication title -
asian journal of computer science and technology
Language(s) - English
Resource type - Journals
eISSN - 2583-7907
pISSN - 2249-0701
DOI - 10.51983/ajcst-2019.8.1.2122
Subject(s) - computer science , semantic similarity , bootstrapping (finance) , artificial intelligence , similarity (geometry) , precision and recall , concept drift , recall , data mining , artificial neural network , class (philosophy) , machine learning , mathematics , linguistics , philosophy , data stream mining , image (mathematics) , econometrics
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. In this research work, a method to minimize semantic drift by identifying the (Drifting Points) DPs and removing the effect introduced by the DPs is proposed. Previous methods for identifying drifting errors can be roughly divided into two categories: (1) multi-class based, and (2) single-class based, according to the settings of Information Extraction systems that adopt them. 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.