Automatic Bibliographic Metadata Extraction Approach

Abstract

Recently, many digital libraries have been constructed and published. Scientific papers often conclude with a section that lists referenced works in the form of a reference list or bibliography. This form of acknowledgment is crucial in helping readers and reviewers to relate the current work to its context within the research community’s discourse. Such bibliographical references that appear in journal articles can provide valuable hints for subsequent information extraction. Therefore, automatic extraction of metadata and bibliographies is widely studies in recent years. Decision tree is now widely used machine learning approach in many areas. This paper applies the Decision tree for bibliographic metadata extraction and the experiment show that decision tree classifier achieves high accuracy result.