Class-based n-gram models of natural language
Computational Linguistics
EACL '99 Proceedings of the ninth conference on European chapter of the Association for Computational Linguistics
Chunking with support vector machines
NAACL '01 Proceedings of the second meeting of the North American Chapter of the Association for Computational Linguistics on Language technologies
Large Margin Methods for Structured and Interdependent Output Variables
The Journal of Machine Learning Research
Use of support vector learning for chunk identification
ConLL '00 Proceedings of the 2nd workshop on Learning language in logic and the 4th conference on Computational natural language learning - Volume 7
Comparisons of sequence labeling algorithms and extensions
Proceedings of the 24th international conference on Machine learning
BioNLP '08 Proceedings of the Workshop on Current Trends in Biomedical Natural Language Processing
Cutting-plane training of structural SVMs
Machine Learning
Word representations: a simple and general method for semi-supervised learning
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Extracting named entities using support vector machines
KDLL'06 Proceedings of the 2006 international conference on Knowledge Discovery in Life Science Literature
Using an ensemble system to improve concept extraction from clinical records
Journal of Biomedical Informatics
DTMBIO 2012: international workshop on data and text mining in biomedical informatics
Proceedings of the 21st ACM international conference on Information and knowledge management
Exploring the effectiveness of medical entity recognition for clinical information retrieval
Proceedings of the 7th international workshop on Data and text mining in biomedical informatics
Unsupervised biomedical named entity recognition: Experiments with clinical and biological texts
Journal of Biomedical Informatics
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Named entity recognition (NER) is an important task for natural language processing (NLP) of clinical text. Conditional Random Fields (CRFs), a sequential labeling algorithm, and Support Vector Machines (SVMs), which is based on large margin theory, are two typical machine learning algorithms that have been widely applied to NER tasks, including clinical entity recognition. However, Structural Support Vector Machines (SSVMs), an algorithm that combines the advantages of both CRFs and SVMs, has not been investigated for clinical text processing. In this study, we applied the SSVMs algorithm to the Concept Extraction task of the 2010 i2b2 clinical NLP challenge, which was to recognize entities of medical problems, treatments, and tests from hospital discharge summaries. Using the same training (N = 27,837) and test (N = 45,009) sets in the challenge, our evaluation showed that the SSVMs-based NER system required less training time, while achieved better performance than the CRFs-based system for clinical entity recognition, when same features were used. Our study also demonstrated that rich features such as unsupervised word representations improved the performance of clinical entity recognition. When rich features were integrated with SSVMs, our system achieved a highest F-measure of 85.74% on the test set of 2010 i2b2 NLP challenge, which outperformed the best system reported in the challenge by 0.5%.