Heterogeneous Supervision for Relation Extraction: A Representation Learning Approach
EMNLP, pp. 46-56, 2017.
Relation extraction is a fundamental task in information extraction. Most existing methods have heavy reliance on annotations labeled by human experts, which are costly and time-consuming. To overcome this drawback, we propose a novel framework, REHession, to conduct relation extractor learning using annotations from heterogeneous informa...More
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