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

文献类型： 会议论文

第一作者： Jerry Junyang Cheung

作者： Jerry Junyang Cheung 1 ; Yue Yu 1 ; Yingjun Mou 1 ; Xiang Chen 2 ; Le Song 3 ; Chao Zhang 1 ; Yuchen Zhuang 1 ; Yinghao Li 1 ;

作者机构： 1.Georgia Institute of Technology, Atlanta, USA

2.Adobe Research, San Jose, USA

3.MBZUAI, Abu Dhabi, UAE ##BioMap, Beijing, China

会议名称： Conference on Empirical Methods in Natural Language Processing

主办单位：

页码： 730-744

摘要： 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 multilevel 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 significantly1.

分类号： tp311

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