数据库名称：CHEMDNER
数据库简述：
所属国家/地区：Netherlands
数据库主要信息：We describe the development of a chemical entity recognition system and its application in the CHEMDNER-patent track of BioCreative 2015. This community challenge includes a Chemical Entity Mention in Patents (CEMP) recognition task and a Chemical Passage Detection (CPD) classification task. We addressed both tasks by an ensemble system that combines a dictionary-based approach with a statistical one. For this purpose the performance of several lexical resources was assessed using Peregrine, our open-source indexing engine. We combined our dictionary-based results on the patent corpus with the results of tmChem, a chemical recognizer using a conditional random field classifier. To improve the performance of tmChem, we utilized three additional features, viz. part-of-speech tags, lemmas and word-vector clusters. When evaluated on the training data, our final system obtained an F-score of 85.21% for the CEMP task, and an accuracy of 91.53% for the CPD task. On the test set, the best system ranked sixth among 21 teams for CEMP with an F-score of 86.82%, and second among nine teams for CPD with an accuracy of 94.23%. The differences in performance between the best ensemble system and the statistical system separately were small.Database URL: http://biosemantics.org/chemdner-patents.
建立年份：2016
联系信息：Contact information
University/Institution:
Erasmus University Rotterdam
Address:
Department of Medical Informatics, Erasmus University Medical Center, PO Box 2040, 3000 CA Rotterdam
City:
Province/State:
Rotterdam
Country/Region:
Netherlands
Contact name (PI/Team):
Jan A. Kors
Contact email (PI/Helpdesk):
j.kors@erasmusmc.nl
