The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships

Erik van Mulligen; A Fourrier-Reglat; D Gurwitz; M Molokhia; A Nieto; Gianluca Trifiro; Jan Kors; LI Furlong

doi:10.1016/j.jbi.2012.04.004

The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships

Erik van Mulligen, A Fourrier-Reglat, D Gurwitz, M Molokhia, A Nieto, Gianluca Trifiro, Jan Kors, LI Furlong

Medical Informatics

Research output: Contribution to journal › Article › Academic › peer-review

113 Citations (Scopus)

Abstract

Corpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug-disorder, drug-target, and target-disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts. (C) 2012 Elsevier Inc. All rights reserved.

Original language	Undefined/Unknown
Pages (from-to)	879-884
Number of pages	6
Journal	Journal of Biomedical Informatics
Volume	45
Issue number	5
DOIs	https://doi.org/10.1016/j.jbi.2012.04.004
Publication status	Published - 2012

Research programs

EMC NIHES-03-77-01

Access to Document

10.1016/j.jbi.2012.04.004

http://hdl.handle.net/1765/37388

Cite this

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title = "The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships",

abstract = "Corpora with specific entities and relationships annotated are essential to train and evaluate text-mining systems that are developed to extract specific structured information from a large corpus. In this paper we describe an approach where a named-entity recognition system produces a first annotation and annotators revise this annotation using a web-based interface. The agreement figures achieved show that the inter-annotator agreement is much better than the agreement with the system provided annotations. The corpus has been annotated for drugs, disorders, genes and their inter-relationships. For each of the drug-disorder, drug-target, and target-disorder relations three experts have annotated a set of 100 abstracts. These annotated relationships will be used to train and evaluate text-mining software to capture these relationships in texts. (C) 2012 Elsevier Inc. All rights reserved.",

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T1 - The EU-ADR corpus: Annotated drugs, diseases, targets, and their relationships

AU - van Mulligen, Erik

AU - Fourrier-Reglat, A

AU - Gurwitz, D

AU - Molokhia, M

AU - Nieto, A

AU - Trifiro, Gianluca

AU - Kors, Jan

AU - Furlong, LI

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