TY - GEN
T1 - Learning Section Weights for Multi-label Document Classification
AU - Moradi Fard, Maziar
AU - Sorolla Bayod, Paula
AU - Motarjem, Kiomars
AU - Alian Nejadi, Mohammad
AU - Akhondi, Saber
AU - Thorne, Camilo
N1 - Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.
PY - 2024
Y1 - 2024
N2 - Multi-label document classification is a traditional task in NLP. Compared to single-label classification, each document can be assigned multiple classes. This problem is crucial in various domains, such as tagging scientific articles. Articles are often structured into several sections such as abstract, title or introduction. Current approaches treat different sections equally for multi-label classification. We argue that this is not a realistic assumption, leading to sub-optimal results. Instead, we propose a new method called Learning Section Weights (LSW), leveraging the contribution of each distinct section for multi-label classification. Via multiple feed-forward layers, LSW learns to assign weights to each section and incorporate the weights in the prediction. We demonstrate our approach in scientific articles. Experimental results on public (arXiv) and private (Elsevier) datasets confirm the superiority of LSW compared to state-of-the-art multi-label document classification methods. In particular, LSW achieves a 1.3% improvement in terms of Macro F-1 while it achieves 1.3% in terms of Macro Recall on the publicly available arXiv dataset.
AB - Multi-label document classification is a traditional task in NLP. Compared to single-label classification, each document can be assigned multiple classes. This problem is crucial in various domains, such as tagging scientific articles. Articles are often structured into several sections such as abstract, title or introduction. Current approaches treat different sections equally for multi-label classification. We argue that this is not a realistic assumption, leading to sub-optimal results. Instead, we propose a new method called Learning Section Weights (LSW), leveraging the contribution of each distinct section for multi-label classification. Via multiple feed-forward layers, LSW learns to assign weights to each section and incorporate the weights in the prediction. We demonstrate our approach in scientific articles. Experimental results on public (arXiv) and private (Elsevier) datasets confirm the superiority of LSW compared to state-of-the-art multi-label document classification methods. In particular, LSW achieves a 1.3% improvement in terms of Macro F-1 while it achieves 1.3% in terms of Macro Recall on the publicly available arXiv dataset.
KW - Classification
KW - Deep Neural Networks
KW - Explainability
UR - http://www.scopus.com/inward/record.url?scp=85205500436&partnerID=8YFLogxK
U2 - 10.1007/978-3-031-70242-6_34
DO - 10.1007/978-3-031-70242-6_34
M3 - Conference contribution
AN - SCOPUS:85205500436
SN - 9783031702419
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 359
EP - 366
BT - Natural Language Processing and Information Systems - 29th International Conference on Applications of Natural Language to Information Systems, NLDB 2024, Proceedings
A2 - Rapp, Amon
A2 - Di Caro, Luigi
A2 - Meziane, Farid
A2 - Sugumaran, Vijayan
PB - Springer Science and Business Media Deutschland GmbH
T2 - 29th International Conference on Natural Language and Information Systems, NLDB 2024
Y2 - 25 June 2024 through 27 June 2024
ER -