A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling
Ye Wang, Xinxin Liu, Wenxin Hu, Tao Zhang · 2022
Document-level relation extraction (RE) aims to identify relations between entities across multiple sentences. Most previous methods focused on document-level RE under full supervision.However, in real-world scenario, it is expensive and difficult to completely label all relations in a document because the number of entity pairs in document-level RE grows quadratically with the number of entities.To solve the common incomplete labeling problem, we propose a unified positive-unlabeled learning frameworkshift and squared ranking loss positive-unlabeled (SSR-PU) learning.We use positive-unlabeled (PU) learning on documentlevel RE for the first time.Considering that labeled data of a dataset may lead to prior shift of unlabeled data, we introduce a PU learning under prior shift of training data.Also, using none-class score as an adaptive threshold, we propose squared ranking loss and prove its Bayesian consistency with multi-label ranking metrics.Extensive experiments demonstrate that our method achieves an improvement of about 14 F1 points relative to the previous baseline with incomplete labeling.In addition, it outperforms previous state-of-the-art results under both fully supervised and extremely unlabeled settings as well. 1