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ORCIDJournal of Cheminformatics, Volume 7 - Supplement
Volume 7, Number S-1, January 2015
- Martin Krallinger
, Florian Leitner, Obdulia Rabal, Miguel Vazquez, Julen Oyarzabal, Alfonso Valencia:
CHEMDNER: The drugs and chemical names extraction challenge. S1 - 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, P. Senthil Nathan, Slavko Zitnik, 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, Ravikumar Komandur Elayavilli, Andre Lamurias, Francisco M. Couto, Hong-Jie Dai, Richard Tzong-Han Tsai, Caglar Ata, Tolga Can, Anabel Usie, Rui Alves, Isabel Segura-Bedmar, Paloma Martínez, Julen Oyarzabal, Alfonso Valencia:
The CHEMDNER corpus of chemicals and drugs and its annotation principles. S2 - Robert Leaman, Chih-Hsuan Wei, Zhiyong Lu:
tmChem: a high performance approach for chemical named entity recognition and normalization. S3 - Yanan Lu, Donghong Ji, Xiaoyuan Yao, Xiaomei Wei, Xiaohui Liang:
CHEMDNER system with mixed conditional random fields and multi-scale word clustering. S4 - Daniel M. Lowe, Roger A. Sayle:
LeadMine: a grammar and dictionary driven approach to entity recognition. S5 - Riza Theresa Batista-Navarro, Rafal Rak, Sophia Ananiadou:
Optimising chemical named entity recognition with pre-processing analytics, knowledge-rich features and heuristics. S6 - David Campos, Sérgio Matos, José Luís Oliveira:
A document processing pipeline for annotating chemical entities in scientific documents. S7 - Buzhou Tang, Yudong Feng, Xiaolong Wang, Yonghui Wu, Yaoyun Zhang, Min Jiang, Jingqi Wang, Hua Xu:
A comparison of conditional random fields and structured support vector machines for chemical entity recognition in biomedical literature. S8 - Tsendsuren Munkhdalai, Meijing Li, Khuyagbaatar Batsuren, Hyeon Park, Nak Choi, Keun Ho Ryu:
Incorporating domain knowledge in chemical and biomedical named entity recognition with word representations. S9 - Saber A. Akhondi, Kristina M. Hettne, Eelke van der Horst, Erik M. van Mulligen, Jan A. Kors:
Recognition of chemical entities: combining dictionary-based and grammar-based approaches. S10 - Shuo Xu, Xin An, Lijun Zhu, Yunliang Zhang, Haodong Zhang:
A CRF-based system for recognizing chemical entity mentions (CEMs) in biomedical literature. S11 - Madian Khabsa, C. Lee Giles:
Chemical entity extraction using CRF and an ensemble of extractors. S12 - Andre Lamurias, João D. Ferreira, Francisco M. Couto:
Improving chemical entity recognition through h-index based semantic similarity. S13 - Hong-Jie Dai, Po-Ting Lai, Yung-Chun Chang, Richard Tzong-Han Tsai:
Enhancing of chemical compound and drug name recognition using representative tag scheme and fine-grained tokenization. S14 - Anabel Usie, Joaquim Cruz, Jorge Comas, Francesc Solsona, Rui Alves:
CheNER: a tool for the identification of chemical entities and their classes in biomedical literature. S15
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