@inproceedings{htait-etal-2016-bilbo,
    title = "Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers",
    author = "Htait, Amal  and
      Fournier, Sebastien  and
      Bellot, Patrice",
    editor = "Calzolari, Nicoletta  and
      Choukri, Khalid  and
      Declerck, Thierry  and
      Goggi, Sara  and
      Grobelnik, Marko  and
      Maegaard, Bente  and
      Mariani, Joseph  and
      Mazo, Helene  and
      Moreno, Asuncion  and
      Odijk, Jan  and
      Piperidis, Stelios",
    booktitle = "Proceedings of the Tenth International Conference on Language Resources and Evaluation ({LREC}'16)",
    month = may,
    year = "2016",
    address = "Portoro{\v{z}}, Slovenia",
    publisher = "European Language Resources Association (ELRA)",
    url = "https://preview.aclanthology.org/fix-sig-urls/L16-1576/",
    pages = "3632--3636",
    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."
}