ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select

Yuchen Zhuang, Yinghao Li, Junyang Zhang, Yue Yu, Yingjun Mou, Xiang Chen, Le Song, Chao Zhang

Abstract
We study the problem of extracting N-ary relation tuples from scientific articles. This task is challenging because the target knowledge tuples can reside in multiple parts and modalities of the document. Our proposed method ReSel decomposes this task into a two-stage procedure that first retrieves the most relevant paragraph/table and then selects the target entity from the retrieved component. For the high-level retrieval stage, ReSel designs a simple and effective feature set, which captures multi-level lexical and semantic similarities between the query and components. For the low-level selection stage, ReSel designs a cross-modal entity correlation graph along with a multi-view architecture, which models both semantic and document-structural relations between entities. Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.
Anthology ID:
2022.emnlp-main.46
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:
730–744
Language:
URL:
https://aclanthology.org/2022.emnlp-main.46
DOI:
10.18653/v1/2022.emnlp-main.46
Bibkey:
Cite (ACL):
Yuchen Zhuang, Yinghao Li, Junyang Zhang, Yue Yu, Yingjun Mou, Xiang Chen, Le Song, and Chao Zhang. 2022. ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select. In Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, pages 730–744, Abu Dhabi, United Arab Emirates. Association for Computational Linguistics.
Cite (Informal):
ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select (Zhuang et al., EMNLP 2022)
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