Snowball: extracting relations from large plain-text collections
DL '00 Proceedings of the fifth ACM conference on Digital libraries
Scaling question answering to the Web
Proceedings of the 10th international conference on World Wide Web
Exploiting redundancy in question answering
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Extracting Patterns and Relations from the World Wide Web
WebDB '98 Selected papers from the International Workshop on The World Wide Web and Databases
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ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
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HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
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AAAI'04 Proceedings of the 19th national conference on Artifical intelligence
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IJCAI'07 Proceedings of the 20th international joint conference on Artifical intelligence
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WISM'12 Proceedings of the 2012 international conference on Web Information Systems and Mining
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Proceedings of the ACM SIGKDD Workshop on Interactive Data Exploration and Analytics
When speed has a price: fast information extraction using approximate algorithms
Proceedings of the VLDB Endowment
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While tuple extraction for a given relation has been an active research area, its dual problem of pattern search-- to find and rank patterns in a principled way-- has not been studied explicitly. In this paper, we propose and address the problem of pattern search, in addition to tuple extraction. As our objectives, we stress reusability for pattern search and scalability of tuple extraction, such that our approach can be applied to very large corpora like the Web. As the key foundation, we propose a conceptual model PRDualRank to capture the notion of precision and recall for both tuples and patterns in a principled way, leading to the "rediscovery" of the Pattern-Relation Duality-- the formal quantification of the reinforcement between patterns and tuples with the metrics of precision and recall. We also develop a concrete framework for PRDualRank, guided by the principles of a perfect sampling process over a complete corpus. Finally, we evaluated our framework over the real Web. Experiments show that on all three target relations our principled approach greatly outperforms the previous state-of-the-art system in both effectiveness and efficiency. In particular, we improved optimal F-score by up to 64%.