SciREX: A Challenge Dataset for Document-Level Information Extraction

• URL: http://arxiv.org/abs/2005.00512v1
• Date: Fri, 1 May 2020 17:30:10 GMT
• Title: SciREX: A Challenge Dataset for Document-Level Information Extraction
• Authors: Sarthak Jain, Madeleine van Zuylen, Hannaneh Hajishirzi, Iz Beltagy
• Abstract summary: It is challenging to create a large-scale information extraction dataset at the document level. We introduce SciREX, a document level IE dataset that encompasses multiple IE tasks. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE.
• Score: 56.83748634747753
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Extracting information from full documents is an important problem in many domains, but most previous work focus on identifying relationships within a sentence or a paragraph. It is challenging to create a large-scale information extraction (IE) dataset at the document level since it requires an understanding of the whole document to annotate entities and their document-level relationships that usually span beyond sentences or even sections. In this paper, we introduce SciREX, a document level IE dataset that encompasses multiple IE tasks, including salient entity identification and document level $N$-ary relation identification from scientific articles. We annotate our dataset by integrating automatic and human annotations, leveraging existing scientific knowledge resources. We develop a neural model as a strong baseline that extends previous state-of-the-art IE models to document-level IE. Analyzing the model performance shows a significant gap between human performance and current baselines, inviting the community to use our dataset as a challenge to develop document-level IE models. Our data and code are publicly available at https://github.com/allenai/SciREX

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