Multi-Task Transfer Learning for Weakly-Supervised Relation Extraction

By Jing Jiang. 2009

85 citations recorded by the source corpus.

Creating labeled training data for relation extraction is expensive. In this paper, we study relation extraction in a special weakly-supervised setting when we have only a few seed instances of the target relation type we want to extract but we also have a large amount of labeled instances of other relation types. Observing that different relation types can share certain common structures, we propose to use a multi-task learning method coupled with human guidance to address this weakly-supervised relation extraction problem. The proposed framework models the commonality among different relation types through a shared weight vector, enables knowledge learned from the auxiliary relation types to be transferred to the target relation type, and allows easy control of the tradeoff between precision and recall. Empirical evaluation on the ACE 2004 data set shows that the proposed method substantially improves over two baseline methods.

Subfields: Artificial Intelligence

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In Artificial Intelligence

1. Jialin Pan
2. Qiang Yang
3. Yu Zhang
4. Mingsheng Long
5. Jianmin Wang
6. Sinno Jialin Pan
7. B. Liu
8. Dongrui Wu

Citations in the corpus

• Feature-based transfer learning with real-world applications (2010, background): “In the fields of Natural Language Processing (NLP), transfer learning, which is known as domain adaptation, has been widely studied for solving various tasks [42], such as name entity recognition [71, 9, 156, 51, 201], part-of-speech tagging [4, 26, 87, 51], sentiment classification [25, 104, 113], ”