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Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study

Author

Listed:
  • Rodrigo Cuéllar Hidalgo

    (Biblioteca Daniel Cosío Villegas, El Colegio de México, Carretera Picacho Ajusco 20, Mexico City 14110, Mexico)

  • Raúl Pinto Elías

    (Tecnológico Nacional de México/CENIDET, Cuernavaca 62490, Mexico)

  • Juan-Manuel Torres-Moreno

    (Laboratoire Informatique d’Avignon, Université d’Avignon, 339 Chemin des Meinajariès, CEDEX 9, 84911 Avignon, France)

  • Osslan Osiris Vergara Villegas

    (Industrial and Manufacturing Engineering Department, Universidad Autónoma de Ciudad Juárez, Ciudad Juárez 32310, Mexico)

  • Gerardo Reyes Salgado

    (Departamento de Informática y Estadística, Universidad Rey Juan Carlos, Av. del Alcalde de Móstoles, 28933 Madrid, Spain)

  • Andrea Magadán Salazar

    (Tecnológico Nacional de México/CENIDET, Cuernavaca 62490, Mexico)

Abstract
In the realm of digital libraries, efficiently managing and accessing scientific publications necessitates automated bibliographic reference segmentation. This study addresses the challenge of accurately segmenting bibliographic references, a task complicated by the varied formats and styles of references. Focusing on the empirical evaluation of Conditional Random Fields (CRF), Bidirectional Long Short-Term Memory with CRF (BiLSTM + CRF), and Transformer Encoder with CRF (Transformer + CRF) architectures, this research employs Byte Pair Encoding and Character Embeddings for vector representation. The models underwent training on the extensive Giant corpus and subsequent evaluation on the Cora Corpus to ensure a balanced and rigorous comparison, maintaining uniformity across embedding layers, normalization techniques, and Dropout strategies. Results indicate that the BiLSTM + CRF architecture outperforms its counterparts by adeptly handling the syntactic structures prevalent in bibliographic data, achieving an F1-Score of 0.96. This outcome highlights the necessity of aligning model architecture with the specific syntactic demands of bibliographic reference segmentation tasks. Consequently, the study establishes the BiLSTM + CRF model as a superior approach within the current state-of-the-art, offering a robust solution for the challenges faced in digital library management and scholarly communication.

Suggested Citation

  • Rodrigo Cuéllar Hidalgo & Raúl Pinto Elías & Juan-Manuel Torres-Moreno & Osslan Osiris Vergara Villegas & Gerardo Reyes Salgado & Andrea Magadán Salazar, 2024. "Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study," Data, MDPI, vol. 9(5), pages 1-24, May.
    • Handle: RePEc:gam:jdataj:v:9:y:2024:i:5:p:71-:d:1397326
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    References listed on IDEAS

    as
    1. Lutz Bornmann & Rüdiger Mutz, 2015. "Growth rates of modern science: A bibliometric analysis based on the number of publications and cited references," Journal of the Association for Information Science & Technology, Association for Information Science & Technology, vol. 66(11), pages 2215-2222, November.
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