Combining SVM classifiers to identify investigator name zones in biomedical articles

ABSTRACT This paper describes an automated system to label zones containing Investigator Names (IN) in biomedical articles, a key item in a MEDLINE® citation. The correct identification of th ese zones is necessary for the subsequent extraction of IN from these zones. A hierarchical classification model is pr oposed using two Support Vector Machine (SVM) classifiers. The first classifier is used to identify an IN zone with high est confidence, and the other cla ssifier identifies the remaining IN zones. Eight sets of word lists are collected to train and test the classifiers, each set containing collections of words ranging from 100 to 1,200. Experiments based on a test set of 105 journal articles show a Precision of 0.88, 0.97 Recall, 0.92 F-Measure, and 0.99 Accuracy. Keywords: Investigator Names, MEDLINE, Support Vector Machine, labeling, text classification, bibliographic information. 1. INTRODUCTION The U.S. National Library of Medicine (NLM) maintains MEDLINE, a heavily used bibliographic database of 20 million citations to the biomedical journal literature. Each citation consists of bibliographic information such as article title, author names, affiliations, the abstract , grant numbers, databank accession number s, etc. While NLM receives most such citations in XML format directly from journal publishers, key bibliographic information is often missing, requiring manual entry. We have therefore developed a system called Publisher Data Review System (PDRS) to automatically extract missing bibliographic information such as grant numbers, grant support information, databank accession numbers, Investigator Names (IN), etc. [1, 2, 3, 4]. In a typical biomedical article, the author zone is located close to the article title, and contains names of the authors. However, due to the increasingly collaborative nature of biomedical research, many investigators from various groups/organizations may collaborate in conducting the res earch. These group/org anization names might also appear in the author or title zones, and the investigators affiliated with them would have their names listed somewhere else within the article, most likely toward the end, close to the references. In articles that list investigators, the number of investigator names average about 40, but may number several hundred. The manual process to enter this data is time-consuming and error-prone. We have therefore proposed a system for automatic extraction of IN consisting of three steps. The system first divides an article into zones, next identifies zones containing IN, and then extracts IN from th ese zones. In this paper, we will focus on the second step, that of identifying (labeling) IN zones. Algorithms commonly used for document labeling or named entity recognition include: Naive Bayes algorithm [5] used for spam emails [6] and Web document classification [7], and S upport Vector Machine (SVM) [8] used to categorize newswire documents, Medical Subject Headings (MeSH) [9], Web documents [10], and Reuters-21578 collection [11]. Since there are various types of zones with IN, supervised learning algorithms are proper in this case. We, therefore, use SVM classifiers in t his work. This paper is organized as follows. The definitions are given in Section 2. The details of our method using SVM classifiers are presented in Section 3. Performance evaluation measures are shown in Section 4. We report experimental results in Section 5, and conclusions in Section 6. *