Cuéllar Hidalgo, R.; Pinto Elías, R.; Torres Moreno, J.M.; Vergara Villegas , O.O.; Reyes Salgado, G.; Magadán Salazar, A. Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study. Data2024, 9, 71.
Cuéllar Hidalgo, R.; Pinto Elías, R.; Torres Moreno, J.M.; Vergara Villegas , O.O.; Reyes Salgado, G.; Magadán Salazar, A. Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study. Data 2024, 9, 71.
Cuéllar Hidalgo, R.; Pinto Elías, R.; Torres Moreno, J.M.; Vergara Villegas , O.O.; Reyes Salgado, G.; Magadán Salazar, A. Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study. Data2024, 9, 71.
Cuéllar Hidalgo, R.; Pinto Elías, R.; Torres Moreno, J.M.; Vergara Villegas , O.O.; Reyes Salgado, G.; Magadán Salazar, A. Neural Architecture Comparison for Bibliographic Reference Segmentation: An Empirical Study. Data 2024, 9, 71.
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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