NDLI: Searching patterns for relation extraction over the web: rediscovering the pattern-relation duality

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Searching patterns for relation extraction over the web: rediscovering the pattern-relation duality

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Searching patterns for relation extraction over the web: rediscovering the pattern-relation duality

Content Provider	ACM Digital Library
Author	Chang, Kevin Chen-Chuan Fang, Yuan
Abstract	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%.
Starting Page	825
Ending Page	834
Page Count	10
File Format	PDF
ISBN	9781450304931
DOI	10.1145/1935826.1935933
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2011-02-09
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Pattern Relation Ranking Web mining Information extraction
Content Type	Text
Resource Type	Article

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