A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling

Ye Wang, Xinxin Liu, Wenxin Hu, Tao Zhang

Abstract
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 framework - shift and squared ranking loss positive-unlabeled (SSR-PU) learning. We use positive-unlabeled (PU) learning on document-level 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.
Anthology ID:
2022.emnlp-main.276
Volume:
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
Month:
December
Year:
2022
Address:
Abu Dhabi, United Arab Emirates
Editors:
Yoav Goldberg, Zornitsa Kozareva, Yue Zhang
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
4123–4135
Language:
URL:
https://aclanthology.org/2022.emnlp-main.276
DOI:
10.18653/v1/2022.emnlp-main.276
Bibkey:
Cite (ACL):
Ye Wang, Xinxin Liu, Wenxin Hu, and Tao Zhang. 2022. A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 4123–4135, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
A Unified Positive-Unlabeled Learning Framework for Document-Level Relation Extraction with Different Levels of Labeling (Wang et al., EMNLP 2022)
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https://preview.aclanthology.org/ingest-acl-2023-videos/2022.emnlp-main.276.pdf
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