The CHEMDNER corpus of chemicals and drugs and its annotation principles

NCUIR > College of Electrical Engineering & Computer Science > Department of Computer Science and information Engineering > journal & Dissertation > Item 987654321/107366

Please use this identifier to cite or link to this item: https://ir.lib.ncu.edu.tw/handle/987654321/107366

Title:	The CHEMDNER corpus of chemicals and drugs and its annotation principles
Authors:	蔡宗翰;Krallinger, Martin;Rabal, Obdulia;Leitner, Florian;Vazquez, Miguel;Salgado, David;Lu, Zhiyong;Leaman, Robert;Lu, Yanan;Ji, Donghong;Lowe, Daniel M;Sayle, Roger A;Batista-Navarro, Riza Theresa;Rak, Rafal;Huber, Torsten;Rocktäschel, Tim;Matos, Sérgio;Campos, David;Tang, Buzhou;Xu, Hua;Munkhdalai, Tsendsuren;Ryu, Keun Ho;Ramanan, SV;Nathan, Senthil;Žitnik, Slavko;Bajec, Marko;Weber, Lutz;Irmer, Matthias;Akhondi, Saber A;Kors, Jan A;Xu, Shuo;An, Xin;Sikdar, Utpal Kumar;Ekbal, Asif;Yoshioka, Masaharu;Dieb, Thaer M;Choi, Miji;Verspoor, Karin;Khabsa, Madian;Giles, C Lee;Liu, Hongfang;Ravikumar, Komandur Elayavilli;Lamurias, Andre;Couto, Francisco M;Dai, Hong-Jie;Tsai, Richard Tzong-Han;Ata, Caglar;Can, Tolga;Usié, Anabel;Alves, Rui;Segura-Bedmar, Isabel;Martínez, Paloma;Oyarzabal, Julen;Valencia, Alfonso
Contributors:	資訊電機學院資訊工程學系
Keywords:	Chemicals;Chemistry;Chemistry and Materials Science;Computational Biology/Bioinformatics;Computer Applications in Chemistry;Documentation and Information in Chemistry;Silver;Theoretical and Computational Chemistry
Date:	2015-01-01
Issue Date:	2026-04-23 14:10:12 (UTC+8)
Publisher:	Chemistry Central;Cham: Springer International Publishing
Abstract:	摘要： The automatic extraction of chemical information from text requires the recognition of chemical entity mentions as one of its key steps. When developing supervised named entity recognition (NER) systems, the availability of a large, manually annotated text corpus is desirable. Furthermore, large corpora permit the robust evaluation and comparison of different approaches that detect chemicals in documents. We present the CHEMDNER corpus, a collection of 10,000 PubMed abstracts that contain a total of 84,355 chemical entity mentions labeled manually by expert chemistry literature curators, following annotation guidelines specifically defined for this task. The abstracts of the CHEMDNER corpus were selected to be representative for all major chemical disciplines. Each of the chemical entity mentions was manually labeled according to its structure-associated chemical entity mention (SACEM) class: abbreviation, family, formula, identifier, multiple, systematic and trivial. The difficulty and consistency of tagging chemicals in text was measured using an agreement study between annotators, obtaining a percentage agreement of 91. For a subset of the CHEMDNER corpus (the test set of 3,000 abstracts) we provide not only the Gold Standard manual annotations, but also mentions automatically detected by the 26 teams that participated in the BioCreative IV CHEMDNER chemical mention recognition task. In addition, we release the CHEMDNER silver standard corpus of automatically extracted mentions from 17,000 randomly selected PubMed abstracts. A version of the CHEMDNER corpus in the BioC format has been generated as well. We propose a standard for required minimum information about entity annotations for the construction of domain specific corpora on chemical and drug entities. The CHEMDNER corpus and annotation guidelines are available at: http://www.biocreative.org/resources/biocreative-iv/chemdner-corpus/ 其他題名： J Cheminform 出版者： Cham: Springer International Publishing 出版日期： 2015 出處： Journal of cheminformatics, 2015, Vol.7 (Suppl 1), p.S2-S2, Article S2 資源來源： Publicly Available Content Database 版權： Krallinger et al.; licensee Springer. 2015. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( ), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver ( ) applies to the data made available in this article, unless otherwise stated. 版權： 2015 Krallinger et al.; licensee Springer. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. 版權： Journal of Cheminformatics is a copyright of Springer, 2015. 版權： Copyright © 2015 Krallinger et al.; licensee Springer. 2015 Krallinger et al.; licensee Springer. 識別號： ISSN: 1758-2946 識別號： EISSN: 1758-2946 識別號： DOI: 10.1186/1758-2946-7-S1-S2 識別號： PMID: 25810773
Appears in Collections:	[Department of Computer Science and information Engineering] journal & Dissertation

Files in This Item:

File	Description	Size	Format
index.html		0Kb	HTML	16	View/Open

社群 sharing

Loading...