ICDM '02 Proceedings of the 2002 IEEE International Conference on Data Mining
Learning block importance models for web pages
Proceedings of the 13th international conference on World Wide Web
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Proceedings of the 27th annual international ACM SIGIR conference on Research and development in information retrieval
Object-level ranking: bringing order to Web objects
WWW '05 Proceedings of the 14th international conference on World Wide Web
Simultaneous record detection and attribute labeling in web data extraction
Proceedings of the 12th ACM SIGKDD international conference on Knowledge discovery and data mining
Proceedings of the 16th international conference on World Wide Web
Webpage understanding: an integrated approach
Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining
Dynamic Hierarchical Markov Random Fields for Integrated Web Data Extraction
The Journal of Machine Learning Research
Closing the loop in webpage understanding
Proceedings of the 17th ACM conference on Information and knowledge management
A demo search engine for products
Proceedings of the 20th international conference companion on World wide web
Towards a theory model for product search
Proceedings of the 20th international conference on World wide web
VisHue: web page segmentation for an improved query interface for medlineplus medical encyclopedia
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
Enhancing product search by best-selling prediction in e-commerce
Proceedings of the 21st ACM international conference on Information and knowledge management
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In this paper we introduce the webpage understanding problem which consists of three subtasks: webpage segmentation, webpage structure labeling, and webpage text segmentation and labeling. The problem is motivated by the search applications we have been working on including Microsoft Academic Search, Windows Live Product Search and Renlifang Entity Relationship Search. We believe that integrated webpage understanding will be an important direction for future research in Web mining.