Causes of Action Identification in Disputes over Construction Project Contracts Using Hierarchical Learning Based on BERT

[1] Wang, Z. (2020). On the constitutionality of compiling a civil code of China: A process map for legislation born out of pragmatism. Singapore: Springer-Verlag Singapore Pte Ltd. Search in Google Scholar

[2] da Costa, L. S., Oliveira, I. L., & Fileto, R. (2023). Text classification using embeddings: A survey. Knowledge and Information Systems, 65(7), 2761-2803. Search in Google Scholar

[3] Graham, S. G., Soltani, H., & Isiaq, O. (2023). Natural language processing for legal document review: Categorising deontic modalities in contracts. Artificial Intelligence and Law. Search in Google Scholar

[4] Chen, H. H., Wu, L., Chen, J. P., Lu, W., & Ding, J. H. (2022). A comparative study of automated legal text classification using random forests and deep learning. Information Processing & Management, 59(2). Search in Google Scholar

[5] Chen, Z., Zhang, H. L., Ye, L., & Li, S. (2021). An approach based on multilevel convolution for sentence-level element extraction of legal text. Wireless Communications & Mobile Computing, 2021, 1-12. Search in Google Scholar

[6] Enamoto, L., Santos, A., Maia, R., Li, W. G., & Rocha, G. P. (2022). Multi-label legal text classification with BiLSTM and attention. International Journal of Computer Applications in Technology, 68(4), 369-378. Search in Google Scholar

[7] ul Hassan, F., Le, T. Y., & Lv, X. (2021). Addressing legal and contractual matters in construction using natural language processing: A critical review. Journal of Construction Engineering and Management, 147(9), 03121004-03121001-03121004-03121017. Search in Google Scholar

[8] Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., et al. (2017, December 04-09). Attention is all you need. Paper presented at the 31st Annual Conference on Neural Information Processing Systems (NIPS), Long Beach, CA. Search in Google Scholar

[9] Devlin, J., Chang, M. W., Lee, K., Toutanova, K., & Assoc Computat, L. (2019, June 02-07). BERT: Pre-training of deep bidirectional transformers for language understanding. Paper presented at the Conference of the North-American-Chapter of the Association-for-Computational-Linguistics - Human Language Technologies (NAACL-HLT), Minneapolis, MN. Search in Google Scholar

[10] Jia, K. L., Meng, F. X., Liang, J., & Gong, P. M. (2023). Text sentiment analysis based on BERT-CBLBGA. Computers & Electrical Engineering, 112. Search in Google Scholar

[11] Imran, A. S., Hodnefjeld, H., Kastrati, Z., Fatima, N., Daudpota, S. M., & Wani, M. A. (2023). Classifying European court of human rights cases using transformer-based techniques. IEEE Access, 11, 55664-55676. Search in Google Scholar

[12] Costa, Y. D. R., Oliveira, H., Nogueira, V., Jr., Massa, L., Yang, X., Barbosa, A., et al. (2023). Automating petition classification in Brazil’s legal system: A two-step deep learning approach. Artificial Intelligence and Law. Search in Google Scholar

[13] Nghiem, M. Q., Baylis, P., Freitas, A., & Ananiadou, S. (2022, June 20-25). Text classification and prediction in the legal domain. Paper presented at the 13th International Conference on Language Resources and Evaluation (LREC), Marseille, France. Search in Google Scholar

[14] Sun, C., Qiu, X. P., Xu, Y. G., & Huang, X. J. (2019, October 18-20). How to fine-tune BERT for text classification? Paper presented at the 18th China National Conference on Computational Linguistics (CCL), Kunming Univ Sci & Technol, Kunming, China. Search in Google Scholar

[15] Mutasodirin, M. A., & Prasojo, R. E. (2021, October 23-25). Investigating text shortening strategy in BERT: Truncation vs summarization. Paper presented at the 2021 International Conference on Advanced Computer Science and Information Systems (ICACSIS). Search in Google Scholar

[16] Susan, S., & Kumar, A. (2021). The balancing trick: Optimized sampling of imbalanced datasets-A brief survey of the recent state of the art. Engineering Reports, 3(4), 1-24. Search in Google Scholar

[17] Douzas, G., Bacao, F., & Last, F. (2018). Improving imbalanced learning through a heuristic oversampling method based on k-means and SMOTE. Information Sciences, 465, 1-20. Search in Google Scholar

[18] Cao, L., & Shen, H. (2022). CSS: Handling imbalanced data by improved clustering with stratified sampling. Concurrency and Computation-Practice & Experience, 34(2), 1-17. Search in Google Scholar

[19] Sadaiyandi, J., Arumugam, P., Sangaiah, A. K., & Zhang, C. (2023). Stratified sampling-based deep learning approach to increase prediction accuracy of unbalanced dataset. Electronics, 12(21), 1-16. Search in Google Scholar

[20] Al-Sabahi, K., & Zhang, Z. P. (2019). Document summarization using sentence-level semantic based on word embeddings. International Journal of Software Engineering and Knowledge Engineering, 29(2), 177-196. Search in Google Scholar

[21] Barrios, F., López, F., Argerich, L., & Wachenchauzer, R. (2016). Variations of the similarity function of TextRank for automated summarization. arXiv:1602.03606. Search in Google Scholar

[22] Yu, S. S., Su, J. D., & Luo, D. (2019). Improving BERT-based text classification with auxiliary sentence and domain knowledge. IEEE Access, 7, 176600-176612. Search in Google Scholar

[23] Qasim, R., Bangyal, W. H., Alqarni, M. A., & Almazroi, A. A. (2022). A fine-tuned BERT-based transfer learning approach for text classification. Journal of Healthcare Engineering, 2022, 1-17. Search in Google Scholar

[24] West, B. T., Sakshaug, J. W., & Aurelien, G. A. S. (2018). Accounting for complex sampling in survey estimation: A review of current software tools. Journal of Official Statistics, 34(3), 721-752. Search in Google Scholar

[25] Tharwat, A. (2021). Classification assessment methods. Applied Computing and Informatics, 17(1), 168-192. Search in Google Scholar

[26] Riyanto, S., Sitanggang, I. S., Djatna, T., & Atikah, T. D. (2023). Comparative analysis using various performance metrics in imbalanced data for multi-class text classification. International Journal of Advanced Computer Science and Applications, 14(6), 1082-1090. Search in Google Scholar

[27] Sokolova, M., & Lapalme, G. (2009). A systematic analysis of performance measures for classification tasks. Information Processing & Management, 45(4), 427-437. Search in Google Scholar

[28] Grandini, M., Bagli, E., & Visani, G. (2020). Metrics for multi-class classification: An overview. arXiv:2008.05756. Search in Google Scholar

[29] Szeghalmy, S., & Fazekas, A. (2023). A comparative study of the use of stratified cross-validation and distribution-balanced stratified cross-validation in imbalanced learning. Sensors, 23(4), 1-27. Search in Google Scholar

[30] Jalilibal, Z., Amiri, A., Castagliola, P., & Khoo, M. B. C. (2021). Monitoring the coefficient of variation: A literature review. Computers & Industrial Engineering, 161. Search in Google Scholar

[31] Xue, Y. B., Wang, X. Y., & Gao, Z. (2019, November 04-06). Multi-classification sentiment analysis based on the fused model. Paper presented at the 31st IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Portland, OR. Search in Google Scholar

[32] Cui, Y., Che, W., Liu, T., Qin, B., Wang, S., & Hu, G. (2020). Revisiting pre-trained models for Chinese natural language processing. Paper presented at the Findings of the Association for Computational Linguistics, ACL 2020: EMNLP 2020, November 16-20, 2020, Virtual, Online. Search in Google Scholar

[33] Abdaoui, A., Pradel, C., & Sigel, G. (2020). Load what you need: Smaller versions of multilingual BERT. arXiv:2010.05609. Search in Google Scholar

[34] Zhao, Z., Li, Y., Hou, C., Zhao, J., Tian, R., Liu, W., et al. (2023). Tencent pretrain: A scalable and flexible toolkit for pre-training models of different modalities. Paper presented at the 61st Annual Meeting of the Association for Computational Linguistics, ACL-DEMO 2023, July 10-12, 2023, Toronto, ON, Canada. Search in Google Scholar

[35] Zhao, Z., Chen, H., Zhang, J., Zhao, X., Liu, T., Lu, W., et al. (2019). UER: An open-source toolkit for pre-training models. Paper presented at the 2019 Conference on Empirical Methods in Natural Language Processing and 9th International Joint Conference on Natural Language Processing, EMNLP-IJCNLP 2019, November 3-7, 2019, Hong Kong, China. Search in Google Scholar

[36] Cao, Y. (2024). The development and improvement of similar case retrieval mechanism. Science Technology and Law (02), 127-138. Search in Google Scholar