Information diffusion through blogspace
Proceedings of the 13th international conference on World Wide Web
Web data extraction based on partial tree alignment
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
Incorporating site-level knowledge to extract structured data from web forums
Proceedings of the 18th international conference on World wide web
Towards combining web classification and web information extraction: a case study
Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
An overview of Microsoft web N-gram corpus and applications
HLT-DEMO '10 Proceedings of the NAACL HLT 2010 Demonstration Session
Automatic extraction of web data records containing user-generated content
CIKM '10 Proceedings of the 19th ACM international conference on Information and knowledge management
LIBSVM: A library for support vector machines
ACM Transactions on Intelligent Systems and Technology (TIST)
Web scale NLP: a case study on url word breaking
Proceedings of the 20th international conference on World wide web
Competition-based user expertise score estimation
Proceedings of the 34th international ACM SIGIR conference on Research and development in Information Retrieval
Interweaving public user profiles on the web
UMAP'10 Proceedings of the 18th international conference on User Modeling, Adaptation, and Personalization
What's in a name?: an unsupervised approach to link users across communities
Proceedings of the sixth ACM international conference on Web search and data mining
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In this paper, we address the problem of author extraction (AE) from user generated content (UGC) pages. Most existing solutions for web information extraction, including AE, adopt supervised approaches, which require expensive manual annotation. We propose a novel unsupervised approach for automatically collecting and labeling training data based on two key observations of author names: (1) people tend to use a single name across sites if their preferred names are available; (2) people tend to create unique usernames to easily distinguish themselves from others, e.g. travelbug61. Our AE solution only requires features extracted from a single UGC page instead of relying on clues from multiple UGC pages. We conducted extensive experiments. (1) The evaluation of automatically labeled author field data shows 95.0% precision. (2) Our method achieves an F1 score of 96.1%, which significantly outperforms a state-of-the-art supervised approach with single page features (F1 score: 68.4%) and has a comparable performance to its multiple page solution (F1 score: 95.4%). (3) We also examine the robustness of our approach on various UGC pages from forums and review sites, and achieve promising results as well.