P
Paloma Martínez
Researcher at Charles III University of Madrid
Publications - 209
Citations - 3473
Paloma Martínez is an academic researcher from Charles III University of Madrid. The author has contributed to research in topics: Web accessibility & Web Accessibility Initiative. The author has an hindex of 25, co-authored 209 publications receiving 2953 citations. Previous affiliations of Paloma Martínez include Complutense University of Madrid & Carlos III Health Institute.
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Journal ArticleDOI
The CHEMDNER corpus of chemicals and drugs and its annotation principles.
Martin Krallinger,Obdulia Rabal,Florian Leitner,Miguel Vazquez,David Salgado,Zhiyong Lu,Robert Leaman,Yanan Lu,Donghong Ji,Daniel M. Lowe,Roger A. Sayle,Riza Theresa Batista-Navarro,Rafal Rak,Torsten Huber,Tim Rocktäschel,Sérgio Matos,David Campos,Buzhou Tang,Hua Xu,Tsendsuren Munkhdalai,Keun Ho Ryu,S. V. Ramanan,Senthil Nathan,Slavko Žitnik,Marko Bajec,Lutz Weber,Matthias Irmer,Saber A. Akhondi,Jan A. Kors,Shuo Xu,Xin An,Utpal Kumar Sikdar,Asif Ekbal,Masaharu Yoshioka,Thaer M. Dieb,Miji Choi,Karin Verspoor,Madian Khabsa,C. Lee Giles,Hongfang Liu,Komandur Elayavilli Ravikumar,Andre Lamurias,Francisco M. Couto,Hong-Jie Dai,Richard Tzong-Han Tsai,Caglar Ata,Tolga Can,Anabel Usié,Rui Alves,Isabel Segura-Bedmar,Paloma Martínez,Julen Oyarzabal,Alfonso Valencia +52 more
TL;DR: The CHEMDNER corpus is presented, 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.
Journal ArticleDOI
The DDI corpus
TL;DR: A manually annotated corpus consisting of 792 texts selected from the DrugBank database and other 233 Medline abstracts, annotated with a total of 18,502 pharmacological substances and 5028 DDIs, including both PK as well as PD interactions, shows that the corpus has enough quality to be used for training and testing NLP techniques applied to the field of Pharmacovigilance.
Proceedings Article
SemEval-2013 Task 9 : Extraction of Drug-Drug Interactions from Biomedical Texts (DDIExtraction 2013)
TL;DR: There were 14 teams who submitted a total of 38 runs for the DDIExtraction 2013 Shared Task challenge and the best result reported was F1 of 71.5% and 65.1% for the second one.
Journal ArticleDOI
Using a shallow linguistic kernel for drug-drug interaction extraction
TL;DR: This work has proposed the first full solution for the automatic extraction of DDIs from biomedical texts and confirms that the shallow linguistic kernel outperforms the authors' previous pattern-based approach.
Journal ArticleDOI
Lessons learnt from the DDIExtraction-2013 Shared Task
TL;DR: This edition has been the first attempt to compare the performance of Information Extraction techniques specific for each of the basic steps of the DDI extraction pipeline and shows advances in the state of the art and demonstrates that significant challenges remain to be resolved.