PDF text classification to leverage information extraction from publication reports

Duy Duc An Bui*, Guilherme Del Fiol, Siddhartha Jonnalagadda

*Corresponding author for this work

    Research output: Contribution to journalArticlepeer-review

    22 Scopus citations


    Objectives: Data extraction from original study reports is a time-consuming, error-prone process in systematic review development. Information extraction (IE) systems have the potential to assist humans in the extraction task, however majority of IE systems were not designed to work on Portable Document Format (PDF) document, an important and common extraction source for systematic review. In a PDF document, narrative content is often mixed with publication metadata or semi-structured text, which add challenges to the underlining natural language processing algorithm. Our goal is to categorize PDF texts for strategic use by IE systems. Methods: We used an open-source tool to extract raw texts from a PDF document and developed a text classification algorithm that follows a multi-pass sieve framework to automatically classify PDF text snippets (for brevity, texts) into TITLE, ABSTRACT, BODYTEXT, SEMISTRUCTURE, and METADATA categories. To validate the algorithm, we developed a gold standard of PDF reports that were included in the development of previous systematic reviews by the Cochrane Collaboration. In a two-step procedure, we evaluated (1) classification performance, and compared it with machine learning classifier, and (2) the effects of the algorithm on an IE system that extracts clinical outcome mentions. Results: The multi-pass sieve algorithm achieved an accuracy of 92.6%, which was 9.7% (p < 0.001) higher than the best performing machine learning classifier that used a logistic regression algorithm. F-measure improvements were observed in the classification of TITLE (+15.6%), ABSTRACT (+54.2%), BODYTEXT (+3.7%), SEMISTRUCTURE (+34%), and MEDADATA (+14.2%). In addition, use of the algorithm to filter semi-structured texts and publication metadata improved performance of the outcome extraction system (F-measure +4.1%, p = 0.002). It also reduced of number of sentences to be processed by 44.9% (p < 0.001), which corresponds to a processing time reduction of 50% (p = 0.005). Conclusions: The rule-based multi-pass sieve framework can be used effectively in categorizing texts extracted from PDF documents. Text classification is an important prerequisite step to leverage information extraction from PDF documents.

    Original languageEnglish (US)
    Pages (from-to)141-148
    Number of pages8
    JournalJournal of Biomedical Informatics
    StatePublished - Jun 1 2016


    • Document analysis
    • Machine learning
    • Natural language processing
    • Text classification

    ASJC Scopus subject areas

    • Computer Science Applications
    • Health Informatics

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