A Unified Multitask Learning Framework for Text Classification and Information Extraction
Keywords:
Multitask Learning,, Text Classification,, Natural Language Processing,, Transformer ArchitecturesAbstract
The rapid expansion of digital textual data has necessitated the development of sophisticated natural language processing systems capable of simultaneously understanding broad document semantics and extracting granular factual details. Traditionally, text classification and information extraction have been treated as disparate computational problems, optimized through isolated pipelines that inherently ignore the latent synergistic relationships between global contextual categorizations and local entity interactions. This paper presents a comprehensive unified multitask learning framework designed to concurrently perform text classification, named entity recognition, and relation extraction within a single end-to-end differentiable architecture. By leveraging a shared deep contextualized encoder based on transformer architectures, the proposed system learns generalized representations that mutually benefit all downstream tasks through implicit parameter regularization and inductive bias transfer. We conduct an extensive empirical investigation across multiple standardized corpora, demonstrating that the unified framework not only matches but frequently exceeds the performance of highly specialized, single-task baseline models. Furthermore, we provide a rigorous theoretical analysis of the loss convergence dynamics and feature space representations, elucidating the mechanisms by which multitask optimization alleviates overfitting on low-resource extraction tasks. The findings advocate for a paradigm shift toward holistic language modeling approaches, offering significant improvements in computational efficiency, deployment scalability, and predictive accuracy for complex enterprise natural language understanding systems.
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1. Zhang, L. (2025). MCP: Control-theoretic orchestration for multimodal large language models. arXiv preprint arXiv:2509.16597
2. Li, L., Wang, Y., Fan, J., Li, J., Qin, S., Wen, Q., & Gao, F. (2025). Quantum knowledge distillation for large language models. arXiv preprint arXiv:2505.13205.
3. Yang, H., Zhang, R., Huang, M., Wang, W., Tang, Y., Li, Y., ... & Zhang, D. (2025). Kvshare: An llm service system with efficient and effective multi-tenant kv cache reuse. arXiv preprint arXiv:2503.16525.
4. Wang, S., Yu, Y., Feldt, R., & Parthasarathy, D. (2025). Automating a complete software test process using llms: An automotive case study. arXiv preprint arXiv:2502.04008.
5. Ma, Y., Qu, D., & Pyrozhenko, M. (2026). Bio-RegNet: A Meta-Homeostatic Bayesian Neural Network Framework Integrating Treg-Inspired Immunoregulation and Autophagic Optimization for Adaptive Community Detection and Stable Intelligence. Biomimetics, 11(1), 48.
6. Shuang, K., Zhou, J., Wang, Q., Qian, B., Wei, Y., & Guo, J. (2025). Utilizing contextual summarizing and reasoning for enhancing document-level event argument extraction. Expert Systems with Applications, 287, 128075.
7. Li, J., Shuang, K., Guo, J., Shi, Z., & Wang, H. (2023). Enhancing semantic relation classification with shortest dependency path reasoning. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 31, 1550-1560.
8. Guo, Y., Sekiguchi, Y., Zeng, W., Ebihara, S., Owaki, D., & Hayashibe, M. (2025). Physics-informed learning framework for lower limb kinematic prediction with sparse sensors and its application in chronic stroke. IEEE Transactions on Neural Systems and Rehabilitation Engineering.
9. Zhang, K., Shuang, K., Yang, X., Yao, X., & Guo, J. (2023, July). What is overlap knowledge in event argument extraction? APE: A cross-datasets transfer learning model for EAE. In Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 393-409).
10. Yifan, O. U. (2018). Participating in Chinese Social Question and Answer Communities: A Case Study of Zhihu. com.
11. Zhang, Y., Carvalho, D., & Freitas, A. (2024, August). Learning disentangled semantic spaces of explanations via invertible neural networks. In Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 2113-2134).
12. Zeng, D., Yang, Y., Tang, Y., Zhao, L., Wang, X., Yun, D., ... & Lin, H. (2025). Shaping school for childhood myopia: the association between floor area ratio of school environment and myopia in China. British Journal of Ophthalmology, 109(1), 146-151.
13. Gao, Z., Qu, Y., & Han, Y. (2025). Cross-Lingual Sponsored Search via Dual-Encoder and Graph Neural Networks for Context-Aware Query Translation in Advertising Platforms. arXiv preprint arXiv:2510.22957.
14. Du, C., Chiang, C. E., Huang, T., & Cui, Z. (2025, September). Adaptive Graph Convolution and Semantic-Guided Attention for Multimodal Risk Detection in Social Networks. In 2025 5th International Conference on Artificial Intelligence, Automation and High Performance Computing (AIAHPC) (pp. 507-512). IEEE.
15. Lu, P., Zhang, Y., Zhang, H., Zheng, J., Tong, K., & Wu, W. (2025, November). Tool-Augmented Hybrid Ensemble Reasoning with Distillation for Bilingual Mathematical Problem Solving. In 2025 4th International Conference on Image Processing, Computer Vision and Machine Learning (ICICML) (pp. 1770-1776). IEEE.
16. Li, Z., Zhang, Y., Pan, T., Sun, Y., Duan, Z., Fang, J., ... & Wang, J. (2025, July). FocusLLM: Precise understanding of long context by dynamic condensing. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers) (pp. 31087-31101).
17. Cui, Z., Huang, T., Chiang, C. E., & Du, C. (2025, August). Toward verifiable misinformation detection: A multi-tool LLM agent framework. In Proceedings of the 2025 International Conference on Generative Artificial Intelligence for Business (pp. 179-185).
18. Fan, D., Sun, B., Dui, H., Zhong, J., Wang, Z., Ren, Y., & Wang, Z. (2022). A modified connectivity link addition strategy to improve the resilience of multiplex networks against attacks. Reliability Engineering & System Safety, 221, 108294.
19. Zhang, W., Zhang, C., Gu, C., Kou, J., Yuan, H., Fang, X., ... & Fang, Y. (2024, October). Hallucination in Large Language Models: From Mechanistic Understanding to Novel Control Frameworks. In 2024 7th International Conference on Universal Village (UV) (pp. 1-36). IEEE.
20. Jiang, A., Huang, J., & Yun, X. (2026, March). Design and empirical research of simulation algorithms for business decision support with BERT and ISAC integration. In International Conference on Cloud Computing, Performance Computing, and Deep Learning (CCPCDL 2025) (Vol. 14131, pp. 331-336). SPIE.
21. Tu, P., Huang, Y., Zheng, F., He, Z., Cao, L., & Shao, L. (2022, June). Guidedmix-net: Semi-supervised semantic segmentation by using labeled images as reference. In Proceedings of the AAAI conference on artificial intelligence (Vol. 36, No. 2, pp. 2379-2387).
22. Zhang, Y., Carvalho, D., & Freitas, A. (2025, July). Quasi-symbolic Semantic Geometry over Transformer-based Variational AutoEncoder. In Proceedings of the 29th Conference on Computational Natural Language Learning (pp. 12-29).
23. Guo, J., Chen, X., Xia, Q., Wang, Z., Ou, J., Qin, L., ... & Tian, W. (2025, July). HASH-RAG: bridging deep hashing with retriever for efficient, fine retrieval and augmented generation. In Findings of the Association for Computational Linguistics: ACL 2025 (pp. 26847-26858).
24. Huang, Y., Zhang, K., Wang, Y., Du, D., Yuan, Y., & Zhao, Z. (2025, June). Enhancing Open-Vocabulary Panoptic Segmentation with Semantic-Guided Q-Tuning. In 2025 IEEE International Conference on Multimedia and Expo (ICME) (pp. 1-6). IEEE.
25. Tang, Y., Kojima, K., Gotoda, M., Nishikawa, S., Hayashi, S., Koike-Akino, T., ... & Klamkin, J. (2020). Design and Optimization of Shallow-Angle Grating Coupler for Vertical Emission from Indium Phosphide Devices.
26. Papineni, K., Roukos, S., Ward, T., & Zhu, W.-J. (2002). BLEU: A method for automatic evaluation of machine translation. In Proceedings of ACL.
27. Huang, T., Cui, Z., Du, C., & Chiang, C. E. (2025, June). CL-ISR: A Contrastive Learning and Implicit Stance Reasoning Framework for Misleading Text Detection on Social Media. In 2025 6th International Conference on Electronic Communication and Artificial Intelligence (ICECAI) (pp. 610-616). IEEE.
Ahmad, N. R. (2025). Voices from the ground: Exploring local perspectives on Pakistan’s mineral extraction and trade under the China-Pakistan Economic Corridor (CPEC). https://doi.org/10.12345/3he3w302
Ahmad, N. R. (2025). Digital transformation and competitive advantage: Leveraging AI in emerging market supply chains. Journal of Emerging Technologies and Supply Chain Management, 4(1), 72–86.
Ahmad, N. R. (2025). The impact of fintech startups on financial innovation and stability in Pakistan’s evolving financial landscape. Pakistan Journal of Financial Innovation and Technology, 3(2), 91–105.
Ahmad, N. R. (2025). Strategic agility in crisis: How Pakistani businesses adapt financially to global disruptions and market shocks. Journal of Business Resilience and Strategic Management, 4(2), 88–102.
Ahmad, N. R. (2025). Sustainable business strategies for achieving competitive advantage in Pakistan’s developing economy. Quarterly Review Journal of Social Sciences. https://doi.org/10.63878/qrjs361
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