TY - CONF ID - www200911 UR - http://www2009.eprints.org/11/ A1 - Zhu, Jun A1 - Nie, Zaiqing A1 - Liu, Xiaojiang A1 - Zhang, Bo A1 - Wen, Ji-Rong Y1 - 2009/04// N2 - Traditional relation extraction methods require pre-specified relations and relation-specific human-tagged examples. Boot- strapping systems significantly reduce the number of train- ing examples, but they usually apply heuristic-based meth- ods to combine a set of strict hard rules, which limit the ability to generalize and thus generate a low recall. Further- more, existing bootstrapping methods do not perform open information extraction (Open IE), which can identify var- ious types of relations without requiring pre-specifications. In this paper, we propose a statistical extraction framework called Statistical Snowball (StatSnowball), which is a boot- strapping system and can perform both traditional relation extraction and Open IE. StatSnowball uses the discriminative Markov logic net- works (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 like- lihood 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 it- erations 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. TI - StatSnowball: a Statistical Approach to Extracting Entity Relationships SP - 101 M2 - Madrid, Spain AV - public EP - 101 T2 - 18th International World Wide Web Conference ER -