Advancing Legal Citation Text Classification A Conv1D-Based Approach for Multi-Class Classification
DOI:
https://doi.org/10.53469/jtpes.2024.04(02).03Keywords:
Legal Citation Text Classification, Multi-class classification, Convolutional Neural Networks (Conv1D)Abstract
The escalating volume and intricacy of legal documents necessitate advanced techniques for automated text classification in the legal domain. Our proposed approach leverages Convolutional Neural Networks (Conv1D), a neural network architecture adept at capturing hierarchical features in sequential data. The incorporation of max-pooling facilitates the extraction of salient features, while softmax activation enables the model to handle the multi-class nature of legal citation categorization. By addressing the limitations identified in previous studies, our model aims to advance the state-of-the-art in legal citation text classification, offering a robust and efficient solution for automated categorization in the legal domain. Our research contributes to the ongoing evolution of NLP applications in the legal field, promising enhanced accuracy and adaptability in the automated analysis of legal texts.
References
Hachey, B., & Grover, C.. Sentence classification experiments for legal text summarisation. In Proceedings of the 17th Annual Conference on Legal Knowledge and Information Systems (Jurix).2004.
Galgani, F., & Hoffmann, A. (2010, December). Lexa: Towards automatic legal citation classification. In Australasian Joint Conference on Artificial Intelligence (pp. 445-454). Berlin, Heidelberg: Springer Berlin Heidelberg.
Galgani, F., Compton, P., & Hoffmann, A.. Lexa: Building knowledge bases for automatic legal citation classification. Expert Systems with Applications, 2015,42(17-18): 6391-6407.
Westermann, H., Savelka, J., Walker, V. R., Ashley, K. D., & Benyekhlef, K. (2019, December). Computer-Assisted Creation of Boolean Search Rules for Text Classification in the Legal Domain. In JURIX (pp. 123-132).
Sil, R., & Roy, A.. Machine Learning Approach for Automated Legal Text Classification. International Journal of Computer Information Systems & Industrial Management Applications, 2021:13.
Aguiar, A., Silveira, R., Pinheiro, V., Furtado, V., & Neto, J. A. (2021, November). Text classification in legal documents extracted from lawsuits in brazilian courts. In Brazilian Conference on Intelligent Systems (pp. 586-600). Cham: Springer International Publishing.
Chen, H., Wu, L., Chen, J., Lu, W., & Ding, J.. A comparative study of automated legal text classification using random forests and deep learning. Information Processing & Management, 2022,59(2): 102798.
Kim, Y. (2014). Convolutional neural networks for sentence classification. arXiv preprint arXiv:1408.5882.
Gholamalinezhad, H., & Khosravi, H. (2020). Pooling methods in deep neural networks, a review. arXiv preprint arXiv:2009.07485.
Huang, J., Gu, S. S., Hou, L., Wu, Y., Wang, X., Yu, H., & Han, J. (2022). Large language models can self-improve. arXiv preprint arXiv:2210.11610.
Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D.. Language models are few-shot learners. Advances in neural information processing systems, 2020,33: 18
Song, X., Wu, D., Zhang, B., Peng, Z., Dang, B., Pan, F., & Wu, Z. (2023). ZeroPrompt: Streaming Acoustic Encoders are Zero-Shot Masked LMs. arXiv e-prints, arXiv-2305.
Zang, H., Li, S., Dong, X., Ma, D., & Dang, B.. Evaluating the Social Impact of AI in Manufacturing: A Methodological Framework for Ethical Production. Academic Journal of Sociology and Management, 2024,2(1), 21-25.
Liu, S., Wu, K., Jiang, C., Huang, B., & Ma, D. (2023). Financial Time-Series Forecasting: Towards Synergizing Performance and Interpretability Within a Hybrid Machine Learning Approach. arXiv preprint arXiv:2401.00534.
Qiao, Y., Jin, J., Ni, F., Yu, J., & Chen, W.. APPLICATION OF MACHINE LEARNING IN FINANCIAL RISK EARLY WARNING AND REGIONAL PREVENTION AND CONTROL: A SYSTEMATIC ANALYSIS BASED ON SHAP. WORLD TRENDS, REALITIES AND ACCOMPANYING PROBLEMS OF DEVELOPMENT, 2023:331.
Qiao, Y., Ni, F., Xia, T., Chen, W., & Xiong, J. (2024, January). AUTOMATIC RECOGNITION OF STATIC PHENOMENA IN RETOUCHED IMAGES: A NOVEL APPROACH. In The 1st International scientific and practical conference “Advanced technologies for the implementation of new ideas”(January 09-12, 2024) Brussels, Belgium. International Science Group. 2024. 349 p. (p. 287).
Ni, F., Zang, H., & Qiao, Y. (2024, January). SMARTFIX: LEVERAGING MACHINE LEARNING FOR PROACTIVE EQUIPMENT MAINTENANCE IN INDUSTRY 4.0. In The 2nd International scientific and practical conference “Innovations in education: prospects and challenges of today”(January 16-19, 2024) Sofia, Bulgaria. International Science Group. 2024. 389 p. (p. 313).
YUXIN, Q., & FANGHAO, N.. COOPERATIVE GENERATIVE ADVERSARIAL NETWORKS: A DEEP DIVE INTO COLLABORATIVE INNOVATION IN GANS. СОВРЕМЕННЫЕ НАУЧНЫЕ ИССЛЕДОВАНИЯ: АКТУАЛЬНЫЕ ВОПРОСЫ, 2023:28.
Shen, Z., Wei, K., Zang, H., Li, L., & Wang, G.. The Application of Artificial Intelligence to The Bayesian Model Algorithm for Combining Genome Data. Academic Journal of Science and Technology, 2023,8(3), 132-135.
Zang, H.. Precision Calibration of Industrial 3D Scanners: An AI-Enhanced Approach for Improved Measurement Accuracy. Global Academic Frontiers, 2024, 2(1): 27-37.
Ni, F., Zang, H., & Qiao, Y. (2024, January). SMARTFIX: LEVERAGING MACHINE LEARNING FOR PROACTIVE EQUIPMENT MAINTENANCE IN INDUSTRY 4.0. In The 2nd International scientific and practical conference “Innovations in education: prospects and challenges of today”(January 16-19, 2024) Sofia, Bulgaria. International Science Group. 2024. 389 p. (p. 313).
Zang, H., Li, S., Dong, X., Ma, D., & Dang, B.. Evaluating the Social Impact of AI in Manufacturing: A Methodological Framework for Ethical Production. Academic Journal of Sociology and Management, 2024 2(1): 21-25.
Jin, X., Manandhar, S., Kafle, K., Lin, Z., & Nadkarni, A. (2022, November). Understanding iot security from a market-scale perspective. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (pp. 1615-1629).
Jin, X., Pei, K., Won, J. Y., & Lin, Z. (2022, November). Symlm: Predicting function names in stripped binaries via context-sensitive execution-aware code embeddings. In Proceedings of the 2022 ACM SIGSAC Conference on Computer and Communications Security (pp. 1631-1645).
Xiao, T., Zeng, L., Shi, X., Zhu, X., & Wu, G. (2022, September). Dual-Graph Learning Convolutional Networks for Interpretable Alzheimer’s Disease Diagnosis. In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 406-415). Cham: Springer Nature Switzerland.
Wang, X., Xiao, T., & Shao, J. (2021). EMRM: Enhanced Multi-source Review-Based Model for Rating Prediction. In Knowledge Science, Engineering and Management: 14th International Conference, KSEM 2021, Tokyo, Japan, August 14–16, 2021, Proceedings, Part III 14 (pp. 487-499). Springer International Publishing.
Wang, X., Xiao, T., Tan, J., Ouyang, D., & Shao, J. (2020). MRMRP: multi-source review-based model for rating prediction. In Database Systems for Advanced Applications: 25th International Conference, DASFAA 2020, Jeju, South Korea, September 24–27, 2020, Proceedings, Part II 25 (pp. 20-35). Springer International Publishing.
Liu, Y., Yang, H., & Wu, C.. Unveiling Patterns: A Study on Semi-Supervised Classification of Strip Surface Defects. IEEE Access,2023, 11, 119933-119946.
Tian, M., Shen, Z., Wu, X., Wei, K., & Liu, Y.. The Application of Artificial Intelligence in Medical Diagnostics: A New Frontier. Academic Journal of Science and Technology, 2023,8(2): 57-61.
Su, J., Nair, S., & Popokh, L. (2023, February). EdgeGym: A Reinforcement Learning Environment for Constraint-Aware NFV Resource Allocation. In 2023 IEEE 2nd International Conference on AI in Cybersecurity (ICAIC) (pp. 1-7). IEEE.
Su, J., Nair, S., & Popokh, L. (2022, November). Optimal Resource Allocation in SDN/NFV-Enabled Networks via Deep Reinforcement Learning. In 2022 IEEE Ninth International Conference on Communications and Networking (ComNet) (pp. 1-7). IEEE.
Popokh, L., Su, J., Nair, S., & Olinick, E. (2021, September). IllumiCore: Optimization Modeling and Implementation for Efficient VNF Placement. In 2021 International Conference on Software, Telecommunications and Computer Networks (SoftCOM) (pp. 1-7). IEEE.
Wang, L., Chen, Z., Zhu, Z., Yu, X., & Mo, J.. Compressive‐sensing swept‐source optical coherence tomography angiography with reduced noise. Journal of Biophotonics, 2022,15(8), e202200087.
Xu, J., Zhu, P., Zhou, Y., & Ren, W. (2023). Distributed Invariant Extended Kalman Filter Using Lie Groups: Algorithm and Experiments. IEEE Transactions on Control Systems Technology.
Zheng, Z., Ying, X., Yao, Z., & Chuah, M. C. (2023). Robustness of Trajectory Prediction Models Under Map-Based Attacks. In Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision (pp. 4541-4550).
Li, Z., Lu, Z., & Yin, M. (2023). Modeling Human Trust and Reliance in AI-Assisted Decision Making: A Markovian Approach.
Li, Z., Zhu, H., Lu, Z., & Yin, M. (2023). Synthetic Data Generation with Large Language Models for Text Classification: Potential and Limitations. arXiv preprint arXiv:2310.07849.
Li, Z., Lu, Z., & Yin, M. (2022, July). Towards Better Detection of Biased Language with Scarce, Noisy, and Biased Annotations. In Proceedings of the 2022 AAAI/ACM Conference on AI, Ethics, and Society (pp. 411-423).
Li, Z., Lu, Z., & Yin, M. (2024). Decoding AI's Nudge: A Unified Framework to Predict Human Behavior in AI-assisted Decision Making. arXiv preprint arXiv:2401.05840.
Chiang, C. W., Lu, Z., Li, Z., & Yin, M. (2023, April). Are Two Heads Better Than One in AI-Assisted Decision Making? Comparing the Behavior and Performance of Groups and Individuals in Human-AI Collaborative Recidivism Risk Assessment. In Proceedings of the 2023 CHI Conference on Human Factors in Computing Systems (pp. 1-18).
Lu, Z., Li, Z., Chiang, C. W., & Yin, M. (2023, August). Strategic adversarial attacks in AI-assisted decision making to reduce human trust and reliance. In Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence (pp. 3020-3028).
Xiong, J., Feng, M., Wang, X., Jiang, C., Zhang, N., & Zhao, Z.. Decoding Sentiments: Enhancing COVID-19 Tweet Analysis through BERT-RCNN Fusion. Journal of Theory and Practice of Engineering Science, 2024,4(01), 86-93.
Liaw, A., & Wiener, M.. Classification and regression by randomForest. R news,2002, 2(3), 18-22.
Jakkula, V.. Tutorial on support vector machine (svm). School of EECS, Washington State University, 2006,37(2.5): 3.
Taud, H., & Mas, J. F.. Multilayer perceptron (MLP). Geomatic approaches for modeling land change scenarios, 2018: 451-455.
Downloads
Published
How to Cite
Issue
Section
License
Copyright (c) 2024 Ying Xie, Zhengning Li, Yibo Yin, Zibu Wei, Guokun Xu, Yang Luo
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
