Military Equipment Entity Extraction Based on Large Language Model
Source abstract
Abstract
The technology of military equipment entity extraction, a crucial component in constructing military knowledge bases, holds significant research value and theoretical importance for guiding the development and improvement of equipment support forces. In the military domain, equipment entities exhibit a phenomenon of nesting, where one entity is contained within another, and abbreviations or codes are frequently used to represent these entities. To address this complexity, this paper proposes a method named CoTNER for extracting entities. Initially, a large-scale language model is used to perform data augmentation with chain-of-thought on the original dataset, providing additional semantic and contextual information. Subsequently, the augmented dataset is fine-tuned on a small-scale language model to adapt it to the task of military equipment entity extraction and to enhance its ability to learn complex rules specific to the domain of military equipment. Additionally, a high-quality data filtering strategy based on instruction-following difficulty scoring is proposed to address the catastrophic forgetting issue that may occur during the fine-tuning of large language models. The experimental results demonstrate that the proposed military equipment entity extraction method outperforms mainstream traditional deep learning methods, validating the effectiveness of CoTNER.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Topic Modeling · Data Quality and Management · Natural Language Processing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Laboratory of Data Science and Information Studies, Beijing Information Science and Technology University, Beijing 100101, China
Funding
National Key Research and Development Program · the Research on the Detection of Government-Hired Internet Trolls in Foreign Social Networks
References (24)
Show source-provided references
1. PaLM: Scaling Language Modeling with Pathways
2. Nurminen, M., Brenner, J., Koponen, M., Latomaa, S., Mikhailov, M., Schierl, F., Ranasinghe, T., Vanmassenhove, E., Vidal, S.A., and Aranberri, N. (2023, January 12–15). Adaptive Machine Translation with Large Language Models. Proceedings of the 24th Annual Conference of the European Association for Machine Translation, Tampere, Finland.
3. Duh, K., Gomez, H., and Bethard, S. (2024, January 16–21). Self-Prompting Large Language Models for Zero-Shot Open-Domain QA. Proceedings of the 2024 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (Volume 1: Long Papers), Mexico City, Mexico.
4. Language Models Are Few-Shot Learners
5. Touvron, H., Martin, L., Stone, K., Albert, P., Almahairi, A., Babaei, Y., Bashlykov, N., Batra, S., Bhargava, P., and Bhosale, S. (2023). Llama 2: Open Foundation and Fine-Tuned Chat Models. arXiv.
6. Chain-of-Thought Prompting Elicits Reasoning in Large Language Models
7. Collins, M., and Singer, Y. (1999, January 21–22). Unsupervised Models for Named Entity Classification. Proceedings of the 1999 Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora, College Park, MD, USA.
8. Mikheev, A., Moens, M., and Grover, C. (1999, January 8–12). Named Entity Recognition without Gazetteers. Association for Computational Linguistics. Proceedings of the Ninth Conference of the European Chapter of the Association for Computational Linguistics, Bergen, Norway.10.3115/977035.977037 ↗
9. Bikel, D.M., Miller, S., Schwartz, R., and Weischedel, R. (April, January 31). Nymble: A High-Performance Learning Name-Finder. Proceedings of the Fifth Conference on Applied Natural Language Processing, Washington, DC, USA.
10. Rapid Development of Hindi Named Entity Recognition Using Conditional Random Fields and Feature Induction10.1145/979872.979879 ↗
11. Natural Language Processing (Almost) from Scratch
12. Ma, X., and Hovy, E. (2016). End-to-End Sequence Labeling via Bi-Directional LSTM-CNNs-CRF. arXiv.10.18653/v1/P16-1101 ↗
13. Li, X., Feng, J., Meng, Y., Han, Q., Wu, F., and Li, J. (2019). A Unified MRC Framework for Named Entity Recognition. arXiv.10.18653/v1/2020.acl-main.519 ↗
14. Goldberg, Y., Kozareva, Z., and Zhang, Y. (2022, January 7–11). Generative Entity Typing with Curriculum Learning. Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, Abu Dhabi, United Arab Emirates.
15. Bouamor, H., Pino, J., and Bali, K. (2023, January 6–10). GPT-RE: In-Context Learning for Relation Extraction Using Large Language Models. Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, Singapore.
16. Rogers, A., Boyd-Graber, J., and Okazaki, N. (2023, January 9–14). Code4Struct: Code Generation for Few-Shot Event Structure Prediction. Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), Toronto, ON, Canada.
17. Wang, S., Sun, X., Li, X., Ouyang, R., Wu, F., Zhang, T., Li, J., and Wang, G. (2023). GPT-NER: Named Entity Recognition via Large Language Models. arXiv.
18. Ashok, D., and Lipton, Z.C. (2023). PromptNER: Prompting For Named Entity Recognition. arXiv.
19. Zhou, W., Zhang, S., Gu, Y., Chen, M., and Poon, H. (2023). UniversalNER: Targeted Distillation from Large Language Models for Open Named Entity Recognition. arXiv.
20. Hu, E.J., Shen, Y., Wallis, P., Allen-Zhu, Z., Li, Y., Wang, S., Wang, L., and Chen, W. (2021). LoRA: Low-Rank Adaptation of Large Language Models. arXiv.
21. Tjong Kim Sang, E.F., and De Meulder, F. (June, January 31). Introduction to the CoNLL-2003 Shared Task: Language-Independent Named Entity Recognition. Proceedings of the Seventh Conference on Natural Language Learning at HLT-NAACL 2003, Edmonton, AB, Canada.10.3115/1119176.1119195 ↗
22. Hockenmaier, J., and Riedel, S. (2013, January 8–9). Towards Robust Linguistic Analysis Using OntoNotes. Proceedings of the Seventeenth Conference on Computational Natural Language Learning, Sofia, Bulgaria.
23. Lino, M.T., Xavier, M.F., Ferreira, F., Costa, R., and Silva, R. (2004, January 26–28). The Automatic Content Extraction (ACE) Program—Tasks, Data, and Evaluation. Proceedings of the Fourth International Conference on Language Resources and Evaluation (LREC’04), Lisbon, Portugal.
24. Christopher, W., Stephanie, S., Julie, M., and Kazuaki, M. (2006). ACE 2005 Multilingual Training Corpus LDC2006T06, Linguistic Data Consortium. 1572864 KB.