期刊
JOURNAL OF CHEMINFORMATICS
卷 7, 期 -, 页码 -出版社
BMC
DOI: 10.1186/1758-2946-7-S1-S11
关键词
-
类别
资金
- Fundamental Research Funds for the Central Universities: Research on Forest Property Circulation Mechanism in Collective Forest Area [JGTD2014-04]
- Beijing Forestry University Young Scientist Fund: Research on Econometric Methods of Auction with their Applications in the Circulation of Collective Forest Right [BLX2011028]
- National Science Foundation of China: Research on Technology Opportunity Detection based on Paper and Patent Information Resources [71403255]
- Key Technologies R&D Program of Chinese 12th Five-Year Plan: STKOS Collaborative Construction System and Auxiliary Tool Development [2011BAH10B02]
- Key Work Project of Institute of Scientific and Technical Information of China (ISTIC): Intelligent Analysis Service Platform and Application Demonstration for Multi-Source Science and Technology Literature in the Era of Big Data [ZD2014-7-1]
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.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据