IDEAS home Printed from https://ideas.repec.org/a/gam/jdataj/v9y2024i5p71-d1397326.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2306-5729/9/5/71/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2306-5729/9/5/71/
    Download Restriction: no
    ---><---

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jdataj:v:9:y:2024:i:5:p:71-:d:1397326. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no bibliographic references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.