Paper: Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction

ACL ID P09-1114
Title Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2009
Authors
  • Jing Jiang (Singapore Management University, Singapore)

Creating labeled training data for rela- tion extraction is expensive. In this pa- per, we study relation extraction in a spe- cial weakly-supervised setting when we have only a few seed instances of the tar- get relation type we want to extract but we also have a large amount of labeled instances of other relation types. Ob- serving that different relation types can share certain common structures, we pro- pose to use a multi-task learning method coupled with human guidance to address this weakly-supervised relation extraction problem. The proposed framework mod- els the commonality among different re- lation types through a shared weight vec- tor, enables knowledge learned from the auxiliary relation types to be transferred to the target relation type, and allows easy control of the trad...