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.
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.
Entities:
Keywords:
chemical named entity recognition; deep learning; mixed conditional random fields; word clustering
Authors: Larry Smith; Lorraine K Tanabe; Rie Johnson nee Ando; Cheng-Ju Kuo; I-Fang Chung; Chun-Nan Hsu; Yu-Shi Lin; Roman Klinger; Christoph M Friedrich; Kuzman Ganchev; Manabu Torii; Hongfang Liu; Barry Haddow; Craig A Struble; Richard J Povinelli; Andreas Vlachos; William A Baumgartner; Lawrence Hunter; Bob Carpenter; Richard Tzong-Han Tsai; Hong-Jie Dai; Feng Liu; Yifei Chen; Chengjie Sun; Sophia Katrenko; Pieter Adriaans; Christian Blaschke; Rafael Torres; Mariana Neves; Preslav Nakov; Anna Divoli; Manuel Maña-López; Jacinto Mata; W John Wilbur Journal: Genome Biol Date: 2008-09-01 Impact factor: 13.583
Authors: Saber A Akhondi; Kristina M Hettne; Eelke van der Horst; Erik M van Mulligen; Jan A Kors Journal: J Cheminform Date: 2015-01-19 Impact factor: 5.514