Work

Simultaneously Self-Attending to All Mentions for Full-Abstract Biological Relation Extraction

Year: 2018

Type: conference-paper

Abstract: Patrick Verga, Emma Strubell, Andrew McCallum. Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). 2018.

Source: Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT)

DOI: 10.18653/v1/n18-1080

Authors: Patrick Verga, Emma Strubell, Andrew McCallum

Institutions: Google (United States), University of Massachusetts Amherst

Language: English

Topic: Biomedical Text Mining and Ontologies

Subfield: Molecular Biology

Field: Biochemistry, Genetics and Molecular Biology

Domain: Life Sciences

Open Access status: hybrid

Funders: National Science Foundation, Chan Zuckerberg Initiative, Division of Materials Research

Awards: DMREF: Collaborative Research: The Synthesis Genome: Data Mining for Synthesis of New Materials, III: Medium: Constructing Knowledge Bases by Extracting Entity-Relations and Meanings from Natural Language via "Universal Schema"

OpenAlex ID: W2788914818

FWCI: 180.4

Cites: 8

Cited by: 294

Related to: 20