Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers

Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers Amal Htait, Sebastien Fournier and Patrice Bellot Aix Marseille University, CNRS, ENSAM, University of Toulon, LSIS UMR 7296,13397, Marseille, France Aix-Marseille University, CNRS, CLEO OpenEdition UMS 3287, 13451, Marseille, France {amal.htait, sebatien.fournier, patrice.bellot}@lsis.org 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. Keywords: Bibliography, Automatic annotation, OpenEdition, Bilbo, SVM, TEI, PDF. 1. Introduction In this paper, we present the automatic identification of bibliographical references’ zone in papers, as far as we know an innovation in its domain. Our work is based on a research and development program presented at LREC (Kim et al., 2012a), it aimed to construct a software environment (BILBO1) enabling the recognition and the automatic structuring of references in scholarly digital documentation (papers, books, etc), independently from their bibliographic styles. The final object is to provide automatic links between each reference and its article or book in OpenEdition site2, which is composed of three different sub-platforms, Revues.org, Hypotheses.org and Calenda. Therefore the automatic recognition of references’ zone is essential as a first step. Although our system works with semi-structured documents, since we only need to distinguish the para- graphs in the paper, but we served of the available corpora based on papers provided as structured files XML/Text En- coding Initiative3 (TEI) by the OpenEdition’s Revues.org platform2. As a first approach, we used an automate graph that can detect patterns of consecutive references and annotate them as the article’s bibliography, and it is realised by the tool Unitex 3.04. On the testing level, we are not capable of detecting long patterns such as bibliographical references’ zones using Unitex 3.0. Therefore, we suggest the use of machine learning technique for the annotation of references, so we can treat each reference apart and not a large amount of data at once. We present our contribution in two sub-tasks: • First Sub-Task: Retrieving references using Support 1bilbo.hypotheses.org 2www.revues.org 3www.tei-c.org/ 4http://www-igm.uniov-mlv.fr/ unitex/ Vector Machines (SVM), due to a model initially cre- ated to differentiate between the footnotes containing or not containing bibliographical references. • Second Sub-Task: Detecting references’ zone of the document, if it exists, as the largest list of consecutive references …

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