Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers

Amal Htait, Sebastien Fournier, Patrice Bellot

Abstract
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.
Anthology ID:
L16-1576
Volume:
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)
Month:
May
Year:
2016
Address:
Portorož, Slovenia
Editors:
Nicoletta Calzolari, Khalid Choukri, Thierry Declerck, Sara Goggi, Marko Grobelnik, Bente Maegaard, Joseph Mariani, Helene Mazo, Asuncion Moreno, Jan Odijk, Stelios Piperidis
Venue:
LREC
SIG:
Publisher:
European Language Resources Association (ELRA)
Note:
Pages:
3632–3636
Language:
URL:
https://aclanthology.org/L16-1576
DOI:
Bibkey:
Cite (ACL):
Amal Htait, Sebastien Fournier, and Patrice Bellot. 2016. Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers. In Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16), pages 3632–3636, Portorož, Slovenia. European Language Resources Association (ELRA).
Cite (Informal):
Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers (Htait et al., LREC 2016)
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https://preview.aclanthology.org/improve-issue-templates/L16-1576.pdf
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