Papers with HA-Net
Dual Supervision Framework for Relation Extraction with Distant Supervision and Human Annotation (2020.coling-main)
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| Challenge: | Existing studies on relation extraction (RE) use labeled training data for relation extraction models but it is expensive and time-consuming. |
| Approach: | They propose a dual supervision framework which utilizes both types of data to train relation extraction models. |
| Outcome: | The proposed framework can predict labels by human annotation and distant supervision without labeling bias since it is expensive and time-consuming. |