Mining Web Sites Using Wrapper Induction, Named Entities, and Post-processing

This paper presents a new framework for extracting information from collections of Web pages across different sites. In the proposed framework, a standard wrapper induction algorithm is used that exploits named entity information that has been previously identified. The idea of post-processing the extraction results is introduced for resolving ambiguous fields and improving the overall extraction performance. Post-processing involves the exploitation of two additional sources of information: field transition probabilities, based on a trained bigram model, and confidence scores, estimated for each field by the wrapper induction system. A multiplicative model that is based on the product of those two probabilities is also considered for post-processing. Experiments were conducted on pages describing laptop products, collected from many different sites and in four different languages. The results highlight the effectiveness of the new framework.