Bilbo-Val: Automatic Identification of Bibliographical Zone in Papers

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.

Divisions: ?? 50811700Jl ??
College of Business and Social Sciences > Aston Institute for Forensic Linguistics
Last Modified: 30 Sep 2024 08:48
Date Deposited: 20 Dec 2022 13:46
Full Text Link: https://hal.arc ... fr/hal-01771689
https://aclanth ... y.org/L16-1576/
https://hal.arc ... 771689/document
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PURE Output Type: Paper
Published Date: 2016-05
Authors: Htait, Amal (ORCID Profile 0000-0003-4647-9996)
Fournier, Sébastien
Bellot, Patrice

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