document: StatSnowball : a statistical approach to extracting entity relationships 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 l1-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 .
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