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

• URL: http://arxiv.org/abs/2210.14427v1
• Date: Wed, 26 Oct 2022 02:28:02 GMT
• Title: ReSel: N-ary Relation Extraction from Scientific Text and Tables by Learning to Retrieve and Select
• Authors: Yuchen Zhuang, Yinghao Li, Jerry Junyang Cheung, Yue Yu, Yingjun Mou, Xiang Chen, Le Song, Chao Zhang
• Abstract summary: We study the problem of extracting N-ary relations from scientific articles. Our proposed method ReSel decomposes this task into a two-stage procedure. Our experiments on three scientific information extraction datasets show that ReSel outperforms state-of-the-art baselines significantly.
• Score: 53.071352033539526
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• 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.

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