Self-supervised relation extraction from the Web
Knowledge and Information Systems
Protecting buying agents in e-marketplaces by direct experience trust modelling
Knowledge and Information Systems
Probabilistic ranking of product features from customer reviews
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Mining Product Reviews in Web Forums
International Journal of Information Retrieval Research
Application of Text Summarization techniques to the Geographical Information Retrieval task
Expert Systems with Applications: An International Journal
Hi-index | 0.01 |
It is difficult to digest the poorly organized and vast amount of information contained in auction Web sites which are fast changing and highly dynamic. We develop a unified framework which can automatically extract product features and summarize hot item features from multiple auction sites. To deal with the irregularity in the layout format of Web pages and harness the uncertainty involved, we formulate the tasks of product feature extraction and hot item feature summarization as a single graph labeling problem using conditional random fields. One characteristic of this graphical model is that it can model the inter-dependence between neighbouring tokens in a Web page, tokens in different Web pages, as well as various information such as hot item features across different auction sites. We have conducted extensive experiments on several real-world auction Web sites to demonstrate the effectiveness of our framework.