Document Type

Conference Proceeding

Publication Title

Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, EMNLP 2022

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 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 significantly.

First Page

730

Last Page

744

Publication Date

12-2022

Keywords

Computational linguistics, Features sets, Multilevels, Multiple modalities, Multiple parts, Relation extraction, Scientific articles, Scientific tables, Scientific texts, Simple++, Two stage procedure

Comments

Preprint version from arXiv

CC BY-NC-SA

Uploaded on June 21, 2024

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