StatSnowball: a statistical approach to extracting entity relationships

  • Authors:

  • Jun Zhu;Zaiqing Nie;Xiaojiang Liu;Bo Zhang;Ji-Rong Wen

  • Affiliations:

  • Tsinghua University, Beijing, China;Microsoft Research Asia, Beijing, China;University of Science and Technology of China, Hefei, China;Tsinghua University, Beijing, China;Microsoft Research Asia, Beijing, China

  • Venue:
  • Proceedings of the 18th international conference on World wide web
  • Year:
  • 2009

Quantified Score

Hi-index 0.00

Visualization

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 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.