title: CHEMDNER system with mixed conditional random fields and multi-scale word clustering.
abstract: BACKGROUND
  The chemical compound and drug name recognition plays an important role in chemical text mining, and it is the basis for automatic relation extraction and event identification in chemical information processing. So a high-performance named entity recognition system for chemical compound and drug names is necessary.
  
  
  METHODS
  We developed a CHEMDNER system based on mixed conditional random fields (CRF) with word clustering for chemical compound and drug name recognition. For the word clustering, we used Brown's hierarchical algorithm and Skip-gram model based on deep learning with massive PubMed articles including titles and abstracts.
  
  
  RESULTS
  This system achieved the highest F-score of 88.20% for the CDI task and the second highest F-score of 87.11% for the CEM task in BioCreative IV. The performance was further improved by multi-scale clustering based on deep learning, achieving the F-score of 88.71% for CDI and 88.06% for CEM.
  
  
  CONCLUSIONS
  The mixed CRF model represents both the internal complexity and external contexts of the entities, and the model is integrated with word clustering to capture domain knowledge with PubMed articles including titles and abstracts. The domain knowledge helps to ensure the performance of the entity recognition, even without fine-grained linguistic features and manually designed rules.
authors: School of Computer, Wuhan University, Wuhan 430072, China.
authors: Yanan
authors: Y
authors: Lu
authors: School of Computer, Wuhan University, Wuhan 430072, China.
authors: Donghong
authors: D
authors: Ji
authors: School of Computer, Wuhan University, Wuhan 430072, China.
authors: Xiaoyuan
authors: X
authors: Yao
authors: School of Computer, Wuhan University, Wuhan 430072, China.
authors: Xiaomei
authors: X
authors: Wei
authors: School of Public Health, Wuhan University, Wuhan 430072, China.
authors: Xiaohui
authors: X
authors: Liang
keywords: chemical named entity recognition; deep learning; mixed conditional random fields; word clustering