NDLI: StatSnowball: a statistical approach to extracting entity relationships

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StatSnowball: a statistical approach to extracting entity relationships

Content Provider	ACM Digital Library
Author	Wen, Ji-Rong Zhu, Jun Nie, Zaiqing Liu, Xiaojiang Zhang, Bo
Abstract	Traditional relation extraction methods require pre-specified relations and relation-specific human-tagged examples. Bootstrapping systems significantly reduce the number of training examples, but they usually apply heuristic-based methods to combine a set of strict hard rules, which limit the ability to generalize and thus generate a low recall. Furthermore, existing bootstrapping methods do not perform open information extraction (Open IE), which can identify various types of relations without requiring pre-specifications. In this paper, we propose a statistical extraction framework called Statistical Snowball (StatSnowball), which is a bootstrapping system and can perform both traditional relation extraction and Open IE. StatSnowball uses the discriminative Markov logic networks (MLNs) and softens hard rules by learning their weights in a maximum likelihood estimate sense. MLN is a general model, and can be configured to perform different levels of relation extraction. In StatSnwoball, pattern selection is performed by solving an $l_{1}-norm$ penalized maximum likelihood estimation, which enjoys well-founded theories and efficient solvers. We extensively evaluate the performance of StatSnowball in different configurations on both a small but fully labeled data set and large-scale Web data. Empirical results show that StatSnowball can achieve a significantly higher recall without sacrificing the high precision during iterations with a small number of seeds, and the joint inference of MLN can improve the performance. Finally, StatSnowball is efficient and we have developed a working entity relation search engine called Renlifang based on it.
Starting Page	101
Ending Page	110
Page Count	10
File Format	PDF
ISBN	9781605584874
DOI	10.1145/1526709.1526724
Language	English
Publisher	Association for Computing Machinery (ACM)
Publisher Date	2009-04-20
Publisher Place	New York
Access Restriction	Subscribed
Subject Keyword	Statistical models Markov logic networks Relationship extraction
Content Type	Text
Resource Type	Article

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