A CRF-based system for recognizing chemical entity mentions (CEMs) in biomedical literature

Springer Science and Business Media LLC - Tập 7 Số S1 - 2015
Shuo Xu1, Xueli An2, Liehuang Zhu1, Yunliang Zhang1, Haodong Zhang3
1Information Technology Supporting Center, Institute of Scientific and Technical Information of China, No. 15 Fuxing Rd., Haidian District, 100038, Beijing, PR China
2School of Economics and Management, Beijing Forestry University, No. 35 Qinghua East Rd., Haidian District, 100083, Beijing, PR China
3Network Center, Science and Technology Daily, No. 15 Fuxing Rd., Haidian District, 100038, Beijing, PR China

Tóm tắt

Abstract Background In order to improve information access on chemical compounds and drugs (chemical entities) described in text repositories, it is very crucial to be able to identify chemical entity mentions (CEMs) automatically within text. The CHEMDNER challenge in BioCreative IV was specially designed to promote the implementation of corresponding systems that are able to detect mentions of chemical compounds and drugs, which has two subtasks: CDI (Chemical Document Indexing) and CEM. Results Our system processing pipeline consists of three major components: pre-processing (sentence detection, tokenization), recognition (CRF-based approach), and post-processing (rule-based approach and format conversion). In our post-challenge system, the cost parameter in CRF model was optimized by 10-fold cross validation with grid search, and word representations feature induced by Brown clustering method was introduced. For the CEM subtask, our official runs were ranked in top position by obtaining maximum 88.79% precision, 69.08% recall and 77.70% balanced F-measure, which were improved further to 88.43% precision, 76.48% recall and 82.02% balanced F-measure in our post-challenge system. Conclusions In our system, instead of extracting a CEM as a whole, we regarded it as a sequence labeling problem. Though our current system has much room for improvement, our system is valuable in showing that the performance in term of balanced F-measure can be improved largely by utilizing large amounts of relatively inexpensive un-annotated PubMed abstracts and optimizing the cost parameter in CRF model. From our practice and lessons, if one directly utilizes some open-source natural language processing (NLP) toolkits, such as OpenNLP, Standford CoreNLP, false positive (FP) rate may be very high. It is better to develop some additional rules to minimize the FP rate if one does not want to re-train the related models. Our CEM recognition system is available at: http://www.SciTeMiner.org/XuShuo/Demo/CEM.

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