A Multi-record Webpage Attribute Extraction Method Combining Active Learning
WEI Jingjing1,2, LIAO Xiangwen3,4, CHEN Qiaoling3,4, MA Feixiang3,4, CHEN Guolong3,4
1.College of Physics and Information Engineering, Fuzhou University, Fuzhou 350116 2.College of Electronics and Information Science, Fujian Jiangxia University, Fuzhou 350108 3.College of Mathematics and Computer Science, Fuzhou University, Fuzhou 350116 4.Fujian Provincial Key Laboratory of Network Computing and Intelligent Information Processing,Fuzhou University, Fuzhou 350116
Abstract：The attribute extraction process can be separated into two phases, alignment and annotation. In the existing alignment methods, different semantic attributes are mistakenly aligned into the same group. Furthermore, to improve the accuracy of semantic annotation, time-consuming manual annotation is often introduced to construct training set. To solve this problem, a multi-record webpage attribute extraction method combining active learning is presented. As for the problem of wrong attribute alignment, shallow semantic is integrated into the alignment approach to relieve the influence of same tags with different semantics. In the semantic annotation phase, textual, visual and global features are extracted for semantic classification and an active learning based SVM classifier is applied to extract structural data. Moreover, a new sample selection strategy is proposed by introducing the global sample information, and more informative samples with lower confidences are selected to be labeled. The experimental results on BBS and microblog datasets confirm the superiority the proposed method.
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2016, Vol. 29 
