Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers

Amal Htait, Sébastien Fournier, Patrice Bellot

Research output: Unpublished contribution to conferenceUnpublished Conference Paperpeer-review


In this paper, we present the automatic annotation of bibliographical references' zone in papers and articles of XML/TEI format. Our work is applied through two phases: first, we use machine learning technology to classify bibliographical and non-bibliographical paragraphs in papers, by means of a model that was initially created to differentiate between the footnotes containing or not containing bibliographical references. The previous description is one of BILBO's features, which is an open source software for automatic annotation of bibliographic reference. Also, we suggest some methods to minimize the margin of error. Second, we propose an algorithm to find the largest list of bibliographical references in the article. The improvement applied on our model results an increase in the model's efficiency with an Accuracy equal to 85.89. And by testing our work, we are able to achieve 72.23% as an average for the percentage of success in detecting bibliographical references' zone.
Original languageEnglish
Number of pages5
Publication statusPublished - May 2016


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