Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
Detection of IUPAC and IUPAC-like chemical names
Bioinformatics
Cascaded classifiers for confidence-based chemical named entity recognition
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
High-Throughput identification of chemistry in life science texts
CompLife'06 Proceedings of the Second international conference on Computational Life Sciences
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Biomedical literature is an important source of information for chemical compounds. However, different representations and nomenclatures for chemical entities exist, which makes the reference of chemical entities ambiguous. Many systems already exist for gene and protein entity recognition, however very few exist for chemical entities. The main reason for this is the lack of corpus to train named entity recognition systems and perform evaluation. In this paper we present a chemical entity recognizer that uses a machine learning approach based on conditional random fields (CRF) and compare the performance with dictionary-based approaches using several terminological resources. For the training and evaluation, a gold standard of manually curated patent documents was used. While the dictionary-based systems perform well in partial identification of chemical entities, the machine learning approach performs better (10% increase in F-score in comparison to the best dictionary-based system) when identifying complete entities.