Locating and parsing bibliographic references in HTML medical articles

Authors:
Jie Zou;Daniel Le;George R. Thoma
Affiliations:
National Institutes of Health, Lister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, 20894, Bethesda, MD, USA;National Institutes of Health, Lister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, 20894, Bethesda, MD, USA;National Institutes of Health, Lister Hill National Center for Biomedical Communications, National Library of Medicine, 8600 Rockville Pike, 20894, Bethesda, MD, USA
Venue:
International Journal on Document Analysis and Recognition - Special Issue DRR09
Year:
2010

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Abstract

The set of references that typically appear toward the end of journal articles is sometimes, though not always, a field in bibliographic (citation) databases. But even if references do not constitute such a field, they can be useful as a preprocessing step in the automated extraction of other bibliographic data from articles, as well as in computer-assisted indexing of articles. Automation in data extraction and indexing to minimize human labor is key to the affordable creation and maintenance of large bibliographic databases. Extracting the components of references, such as author names, article title, journal name, publication date and other entities, is therefore a valuable and sometimes necessary task. This paper describes a two-step process using statistical machine learning algorithms, to first locate the references in HTML medical articles and then to parse them. Reference locating identifies the reference section in an article and then decomposes it into individual references. We formulate this step as a two-class classification problem based on text and geometric features. An evaluation conducted on 500 articles drawn from 100 medical journals achieves near-perfect precision and recall rates for locating references. Reference parsing identifies the components of each reference. For this second step, we implement and compare two algorithms. One relies on sequence statistics and trains a Conditional Random Field. The other focuses on local feature statistics and trains a Support Vector Machine to classify each individual word, followed by a search algorithm that systematically corrects low confidence labels if the label sequence violates a set of predefined rules. The overall performance of these two reference-parsing algorithms is about the same: above 99% accuracy at the word level, and over 97% accuracy at the chunk level.