Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Advances in Automatic Text Summarization
Advances in Automatic Text Summarization
Learning from Labeled and Unlabeled Data using Graph Mincuts
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Kernel methods for relation extraction
The Journal of Machine Learning Research
Mining and summarizing customer reviews
Proceedings of the tenth ACM SIGKDD international conference on Knowledge discovery and data mining
A Probabilistic Approach for Adapting Information Extraction Wrappers and Discovering New Attributes
ICDM '04 Proceedings of the Fourth IEEE International Conference on Data Mining
Sentiment Mining in WebFountain
ICDE '05 Proceedings of the 21st International Conference on Data Engineering
Price prediction and insurance for online auctions
Proceedings of the eleventh ACM SIGKDD international conference on Knowledge discovery in data mining
Adaptive information extraction: core technologies for information agents
Intelligent information agents
Data & Knowledge Engineering
Opinion mining and summarization of reviews in web forums
Proceedings of the Third Annual ACM Bangalore Conference
Probabilistic ranking of product features from customer reviews
IbPRIA'11 Proceedings of the 5th Iberian conference on Pattern recognition and image analysis
Training conditional random fields with unlabeled data and limited number of labeled examples
ICMLC'05 Proceedings of the 4th international conference on Advances in Machine Learning and Cybernetics
Mining Product Reviews in Web Forums
International Journal of Information Retrieval Research
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Online auction Web sites are fast changing, highly dynamic, and complex as they involve tremendous sellers and potential buyers, as well as a huge amount of items listed for bidding. We develop a two-phase framework which aims at mining and summarizing hot items from multiple auctionWeb sites to assist decision making. The objective of the first phase is to automatically extract the product features and product feature values of the items from the descriptions provided by the sellers. We design a HMM-based learning method to train an extended HMM model which can adapt to the unseen Web page from which the information is extracted. The goal of the second phase is to discover and summarize the hot items based on the extracted information. We formulate the hot item mining task as a semi-supervised learning problem and employ the graph mincuts algorithm to accomplish this task. The summary of the hot items is then generated by considering the frequency and the position of the product features being mentioned in the descriptions. We have conducted extensive experiments from several real-world auction Web sites to demonstrate the effectiveness of our framework.