document: Simultaneous record detection and attribute labeling in web data extraction Recent work has shown the feasibility and promise of template-independent Web data extraction . However , existing approaches use decoupled strategies - attempting to do data record detection and attribute labeling in two separate phases . In this paper , we show that separately extracting data records and attributes is highly ineffective and propose a probabilistic model to perform these two tasks simultaneously . In our approach , record detection can benefit from the availability of semantics required in attribute labeling and , at the same time , the accuracy of attribute labeling can be improved when data records are labeled in a collective manner . The proposed model is called Hierarchical Conditional Random Fields . It can efficiently integrate all useful features by learning their importance , and it can also incorporate hierarchical interactions which are very important for Web data extraction . We empirically compare the proposed model with existing decoupled approaches for product information extraction , and the results show significant improvements in both record detection and attribute labeling .
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