Cuéllar-Hidalgo, R.; Pinto-Elías, R.; Torres-Moreno, J.; Vergara Villegas, O.O.; Reyes-Salgado, G.; Magadan-Salazar, A. Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study. Preprints2024, 2024030982. https://doi.org/10.20944/preprints202403.0982.v1
APA Style
Cuéllar-Hidalgo, R., Pinto-Elías, R., Torres-Moreno, J., Vergara Villegas, O.O., Reyes-Salgado, G., & Magadan-Salazar, A. (2024). Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study. Preprints. https://doi.org/10.20944/preprints202403.0982.v1
Chicago/Turabian Style
Cuéllar-Hidalgo, R., Gerardo Reyes-Salgado and Andrea Magadan-Salazar. 2024 "Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study" Preprints. https://doi.org/10.20944/preprints202403.0982.v1
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.
Keywords
Reference Mining; BiLSTM; Transformers; Byte-Pair Encoding; Conditional Random Fields
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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