The CHEMDNER corpus of chemicals and drugs and its annotation principles

Paper Info

Title
The CHEMDNER corpus of chemicals and drugs and its annotation principles

Abstract
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/

Year	DOI	Venue
2015	10.1186/1758-2946-7-S1-S2	JOURNAL OF CHEMINFORMATICS
Keywords	Field	DocType
named entity recognition, BioCreative, text mining, chemical entity recognition, machine learning, chemical indexing, ChemNLP	Data mining,Annotation,Information retrieval,Identifier,Computer science,Text corpus,Bioinformatics,Named-entity recognition	Journal
Volume	Issue	ISSN
7	S1	1758-2946
Citations	PageRank	References
72	2.17	36
Authors
53

Authors (53 rows)

Cited by (72 rows)

References (36 rows)

Name	Order	Citations	PageRank
Martin Krallinger	1	763	35.65
Obdulia Rabal	2	162	8.61
Florian Leitner	3	362	14.92
Miguel Vazquez	4	228	8.54
David Salgado	5	246	9.78
Zhiyong Lu	6	2735	171.27
Robert Leaman	7	914	39.98
Yanan Lu	8	97	4.02
Donghong Ji	9	892	120.08
Daniel M Lowe	10	72	2.17
Roger A Sayle	11	72	2.50
Riza Theresa Batista-Navarro	12	72	2.17
Rafal Rak	13	382	18.30
Torsten Huber	14	72	2.17
Tim Rocktäschel	15	72	2.17
Sérgio Matos	16	415	29.51
David Campos	17	72	2.17
Buzhou Tang	18	368	34.04
Hua Xu	19	77	3.27
Tsendsuren Munkhdalai	20	169	13.49
Keun Ho Ryu	21	81	4.39
S V Ramanan	22	72	2.17
Senthil Nathan	23	122	5.37
Slavko Zitnik	24	94	6.68
Marko Bajec	25	465	34.56
Lutz Weber	26	76	2.60
Matthias Irmer	27	72	2.17
Saber A. Akhondi	28	124	9.40
Jan A Kors	29	72	2.17
Shuo Xu	30	72	2.17
Xin An	31	72	2.50
Utpal Kumar Sikdar	32	72	2.17
Asif Ekbal	33	737	119.31
Masaharu Yoshioka	34	368	41.40
Thaer M Dieb	35	72	2.17
Miji Choi	36	77	3.39
Karin Verspoor	37	993	78.54
Madian Khabsa	38	237	18.81
C. Lee Giles	39	11154	1549.48
Hongfang Liu	40	1479	160.66
Komandur Elayavilli Ravikumar	41	72	2.17
Andre Lamurias	42	83	9.32
Francisco M. Couto	43	982	72.63
Hong-Jie Dai	44	288	21.58
Richard Tzong-Han Tsai	45	72	2.84
Caglar Ata	46	72	2.17
Tolga Can	47	268	16.39
Anabel Usié	48	100	3.88
Rui Alves	49	196	32.99
Isabel Segura-Bedmar	50	435	30.96

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