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Investigator Name Recognition From Medical Journal Articles: A Comparative Study of SVM and Structural SVM
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.