Now showing items 1-1 of 1

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

      Krallinger, Martin; Rabal, Obdulia; Leitner, Florian; Vázquez, 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; Ho Ryu, Keun; Ramanan, SV; Nathan, Senthil; Žitnik, Slavko; Bajec, Marko; Weber, Lutz; Irmer, Matthias; Akhondi, Saber A.; Kors, Jan A.; Xu, Shuo; An, Xin; Kumar Sikdar, Utpal; Ekbal, Asif; Yoshioka, Masaharu; Dieb, Thaer M.; Choi, Miji; Verspoor, Karin; Khabsa, Madian; Lee Giles, C.; Liu, Hongfang; Elayavilli Ravikumar, Komandur; Lamurias, Andre; Coute, Francisco M.; Dai, Hong Jie; Tzong Han Tsai, Richard; Ata, Caglar; Can, Tolga; Usié Chimenos, Anabel; Alves, Rui; Segura Bedmar, Isabel; Martínez, Paloma; Oyarzabal, Julen; Valencia, Alfonso (BioMed Central, 2015)
      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 ...