A probabilistic model with multi-dimensional features for object extraction
To identify recruitment information in different domains, we propose a novel model of hierarchical tree-structured conditional random fields (HT-CRFs). In our approach, first, the concept of a Web object (WOB) is discussed for the description of special Web information. Second, in contrast to traditional methods, the Boolean model and multi-rule are introduced to denote a one-dimensional text feature for a better representation of Web objects. Furthermore, a two-dimensional semantic texture feature is developed to discover the layout of a WOB, which can emphasize the structural attributes and the specific semantics term attributes of WOBs. Third, an optimal WOB information extraction (IE) based on HT-CRF is performed, addressing the problem of a model having an excessive dependence on the page structure and optimizing the efficiency of the model's training. Finally, we compare the proposed model with existing decoupled approaches for WOB IE. The experimental results show that the accuracy rate of WOB IE is significantly improved and that time complexity is reduced.
information extraction (ie) feature extraction conditional random fields (crfs)