Identification (biology)

Physics

Geology

Salient

Biology

Optics

Oceanography

World Wide Web

Baseline (sea)

Relationship extraction

Artificial intelligence

Management

Economics

Set (abstract data type)

Focus (optics)

Information extraction

Programming language

Natural language processing

Information retrieval

Code (set theory)

Paragraph

Sentence

Computer science

Task (project management)

Botany

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 .

SciREX: A Challenge Dataset for Document-Level Information Extraction