The nature of statistical learning theory
The nature of statistical learning theory
Machine Learning
An Algorithm that Learns What‘s in a Name
Machine Learning - Special issue on natural language learning
An introduction to support Vector Machines: and other kernel-based learning methods
An introduction to support Vector Machines: and other kernel-based learning methods
A Tutorial on Support Vector Machines for Pattern Recognition
Data Mining and Knowledge Discovery
Text Categorization with Suport Vector Machines: Learning with Many Relevant Features
ECML '98 Proceedings of the 10th European Conference on Machine Learning
Maximum Entropy Markov Models for Information Extraction and Segmentation
ICML '00 Proceedings of the Seventeenth International Conference on Machine Learning
On the algorithmic implementation of multiclass kernel-based vector machines
The Journal of Machine Learning Research
Support vector machine learning for interdependent and structured output spaces
ICML '04 Proceedings of the twenty-first international conference on Machine learning
Introduction: named entity recognition in biomedicine
Journal of Biomedical Informatics - Special issue: Named entity recognition in biomedicine
Japanese Named Entity extraction with redundant morphological analysis
NAACL '03 Proceedings of the 2003 Conference of the North American Chapter of the Association for Computational Linguistics on Human Language Technology - Volume 1
CONLL '03 Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003 - Volume 4
Boosting performance of bio-entity recognition by combining results from multiple systems
Proceedings of the 5th international workshop on Bioinformatics
Comparisons of sequence labeling algorithms and extensions
Proceedings of the 24th international conference on Machine learning
Introduction to the bio-entity recognition task at JNLPBA
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Annotating multiple types of biomedical entities: a single word classification approach
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Biomedical named entity recognition using conditional random fields and rich feature sets
JNLPBA '04 Proceedings of the International Joint Workshop on Natural Language Processing in Biomedicine and its Applications
Cutting-plane training of structural SVMs
Machine Learning
Extracting person names from diverse and noisy OCR text
AND '10 Proceedings of the fourth workshop on Analytics for noisy unstructured text data
Proceedings of the 12th ACM/IEEE-CS joint conference on Digital Libraries
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Automated extraction of bibliographic information from journal articles is key to the affordable creation and maintenance of citation databases, such as MEDLINE®. A newly required bibliographic field in this database is "Investigator Names": names of people who have contributed to the research addressed in the article, but who are not listed as authors. Since the number of such names is often large, several score or more, their manual entry is prohibitive. The automated extraction of these names is a problem in Named Entity Recognition (NER), but differs from typical NER due to the absence of normal English grammar in the text containing the names. In addition, since MEDLINE conventions require names to be expressed in a particular format, it is necessary to identify both first and last names of each investigator, an additional challenge. We seek to automate this task through two machine learning approaches: Support Vector Machine and structural SVM, both of which show good performance at the word and chunk levels. In contrast to traditional SVM, structural SVM attempts to learn a sequence by using contextual label features in addition to observational features. It outperforms SVM at the initial learning stage without using contextual observation features. However, with the addition of these contextual features from neighboring tokens, SVM performance improves to match or slightly exceed that of the structural SVM.