A Method for Dynamic Expansion of Domain Dictionaries and Weakly Labeled Named Entity Recognition for Low-Resource Specialized Corpora
Main Article Content
Abstract
In order to solve the problems of domain entity lack and high noise, little human supervision in low resource specialized corpora, this work adopts the method of multi-feature fusion of dynamic domain dictionary expansion. The method includes dictionary matching, semantic confidence filtering and confidence-weighted named entity recognition, and realizes the mutual iteration between the dictionary and the recognition model by feeding back the entity. The results demonstrate that, the dynamic expansion raised the entity coverage from 38.10% to 77.22% while the accuracy of adding new entities is 94.67% for the dynamic expansion; the F1 score of the confidence based filtering for weakly labelled data is 84.68%, and the Micro-F1 score of the complete model is 89.17%, which is 13.28 percentage points better than baseline model under 1% human annotation. It shows that the approach proposed in this study is able to complement the long tail specific entities while keeping noise from weak labels under control and the entity recognition performance still good under low level manual annotation.
Downloads
Article Details
This work is licensed under a Creative Commons Attribution 4.0 International License.
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
References
A. E. Torres, E. S. de Moura, A. S. da Silva, et al., “An experimental study on data augmentation techniques for named entity recognition on low-resource domains,” Natural Language Processing, vol. 32, no. 2, pp. 184–203, 2026.
K. Nastou, M. Koutrouli, S. Pyysalo, et al., “Improving dictionary-based named entity recognition with deep learning,” Bioinformatics, 40(Supplement_2): ii45-ii52, 2024.
M. Bali and S. P. Anandaraj, “Reinforcement learning based distantly supervised biomedical named entity recognition,” Intelligent Decision Technologies, vol. 17, no. 2, pp. 317–330, 2023.
W. L. Seow, I. Chaturvedi, A. Hogarth, et al., “A review of named entity recognition: from learning methods to modelling paradigms and tasks,” Artificial Intelligence Review, vol. 58, no. 10, p. 315, 2025.
R. Zanoli, A. Lavelli, D. V. do Amarante, et al., “Assessment of the E3C corpus for the recognition of disorders in clinical texts,” Natural Language Engineering, vol. 30, no. 4, pp. 851–869, 2024.
I. Belhajem, “Effects of Multiple Annotation Schemes on Arabic Named Entity Recognition,” Engineering, Technology & Applied Science Research, vol. 14, no. 5, pp. 17060–17067, 2024.
A. S. Behr, M. Völkenrath, and N. Kockmann, “Ontology extension with NLP-based concept extraction for domain experts in catalytic sciences,” Knowledge and Information Systems, vol. 65, no. 12, pp. 5503–5522, 2023.
I. Morazzoni, V. Scotti, and R. Tedesco, “Def2Vec: you shall know a word by its definition,” International Journal of Speech Technology, vol. 27, no. 4, pp. 887–899, 2024.
M. A. Marjan and T. Amagasa, “Domain-adaptive entity recognition: un-veiling the potential of CSER in cybersecurity and beyond,” International Journal of Machine Learning and Cybernetics, vol. 16, no. 5, pp. 2849– 2867, 2025.
U. Etudo and V. Y. Yoon, “Ontology-based information extraction for labeling radical online content using distant supervision,” Information Systems Research, vol. 35, no. 1, pp. 203–225, 2024.
A. O. B. ¸Sapcı, H. Kemik, R. Yeniterzi, et al., “Focusing on potential named entities during active label acquisition,” Natural Language Engineering, vol. 30, no. 3, pp. 602–624, 2024.
S. Arslan, “Application of BiLSTM-CRF model with different embeddings for product name extraction in unstructured Turkish text,” Neural Computing and Applications, vol. 36, no. 15, pp. 8371–8382, 2024.
Á. García-Barragán, A. Sakor, M. E. Vidal, et al., “NSSC: a neuro-symbolic AI system for enhancing accuracy of named entity recognition and linking from oncologic clinical notes,” Medical & biological engineering & computing, vol. 63, no. 3, pp. 749–772, 2025.
A. Alotaibi, F. Nadeem, and M. Hamdy, “Weakly supervised deep learning for arabic tweet sentiment analysis on education reforms: Leveraging pre-trained models and llms with snorkel,” IEEE Access, vol. 13, pp. 30523– 30542, 2025.
B. S. Lancheros, G. Corpas Pastor, and R. Mitkov, “Data augmentation and transfer learning for cross-lingual Named Entity Recognition in the biomedical domain,” Language Resources and Evaluation, vol. 59, no. 2, pp. 665–684, 2025.
C. Çetinda˘g, B. Yazıcıo˘glu, and A. Koç, “Named-entity recognition in Turkish legal texts,” Natural Language Engineering, vol. 29, no. 3, pp. 615–642, 2023.
I. Belhajem, “Voting Strategies for Arabic Named Entity Recognition using Annotation Schemes,” Engineering, Technology & Applied Science Research, vol. 14, no. 6, pp. 17690–17695, 2024.
A. A. Abdullahi, M. C. Ganiz, U. Koç, et al., “Deep learning for named entity recognition in Turkish radiology reports,” Diagnostic and Interventional Radiology, vol. 31, no. 5, p. 430, 2025.
A. Gopalakrishnan, K. P. Soman, S. Rajendran, et al., “Efficient text analysis: a BERT-based approach to named entity recognition (NER) and classification for Malayalam language,” International Journal of Information Technology, vol. 17, no. 7, pp. 4021–4027, 2025.
D. Premasiri, T. Ranasinghe, R. Mitkov, et al., “Survey on legal information extraction: current status and open challenges: D. Premasiri et al,” Knowledge and Information Systems, vol. 67, no. 12, pp. 11287–11358, 2025.
R. M. Murphy, D. A. Dongelmans, N. F. de Keizer, et al., “Creation of a gold standard Dutch corpus of clinical notes for adverse drug event detection: the Dutch ADE corpus,” Language Resources and Evaluation, vol. 59, no. 3, pp. 2763–2779, 2025.