A study of deep semantic matching in question-and-answer events in civil litigation in the environmental justice system

[1] Mandal, A., Chaki, R., Saha, S., Ghosh, K., Pal, A., & Ghosh, S. (2017, November). Measuring similarity among legal court case documents. In Proceedings of the 10th annual ACM India compute conference (pp. 1-9). Search in Google Scholar

[2] Sugathadasa, K., Ayesha, B., de Silva, N., Perera, A. S., Jayawardana, V., Lakmal, D., & Perera, M. (2019). Legal document retrieval using document vector embeddings and deep learning. In Intelligent Computing: Proceedings of the 2018 Computing Conference, Volume 2 (pp. 160-175). Springer International Publishing. Search in Google Scholar

[3] Oksanen, A., Tamper, M., Tuominen, J., Mäkelä, E., Hietanen, A., & Hyvönen, E. (2019). Semantic Finlex: Transforming, publishing, and using Finnish legislation and case law as linked open data on the web. In Knowledge of the Law in the Big Data Age (pp. 212-228). IOS Press. Search in Google Scholar

[4] Zhang, N., Pu, Y. F., Yang, S. Q., Zhou, J. L., & Gao, J. K. (2017). An ontological Chinese legal consultation system. IEEE Access, 5, 18250-18261. Search in Google Scholar

[5] Bartolini, C., Giurgiu, A., Lenzini, G., & Robaldo, L. (2017). Towards legal compliance by correlating standards and laws with a semi-automated methodology. In BNAIC 2016: Artificial Intelligence: 28th Benelux Conference on Artificial Intelligence, Amsterdam, The Netherlands, November 10-11, 2016, Revised Selected Papers 28 (pp. 47-62). Springer International Publishing. Search in Google Scholar

[6] Merchant, K., & Pande, Y. (2018, September). Nlp based latent semantic analysis for legal text summarization. In 2018 international conference on advances in computing, communications and informatics (ICACCI) (pp. 1803-1807). IEEE. Search in Google Scholar

[7] Sansone, C., & Sperli, G. (2022). Legal information retrieval systems: State-of-the-art and open issues. Information Systems, 106, 101967. Search in Google Scholar

[8] Zhu, J., Wu, J., Luo, X., & Liu, J. (2024). Semantic matching based legal information retrieval system for COVID-19 pandemic. Artificial Intelligence and Law, 32(2), 397-426. Search in Google Scholar

[9] Yu, W., Sun, Z., Xu, J., Dong, Z., Chen, X., Xu, H., & Wen, J. R. (2022, July). Explainable legal case matching via inverse optimal transport-based rationale extraction. In Proceedings of the 45th international ACM SIGIR conference on research and development in information retrieval (pp. 657-668). Search in Google Scholar

[10] Zhong, H., Xiao, C., Tu, C., Zhang, T., Liu, Z., & Sun, M. (2020, July). How Does NLP Benefit Legal System: A Summary of Legal Artificial Intelligence. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics (pp. 5218-5230). Search in Google Scholar

[11] Sleimi, A., Sannier, N., Sabetzadeh, M., Briand, L., & Dann, J. (2018, August). Automated extraction of semantic legal metadata using natural language processing. In 2018 IEEE 26th International Requirements Engineering Conference (RE) (pp. 124-135). IEEE. Search in Google Scholar

[12] Fawei, B., Pan, J. Z., Kollingbaum, M., & Wyner, A. Z. (2019). A semi-automated ontology construction for legal question answering. New Generation Computing, 37(4), 453-478. Search in Google Scholar

[13] Kien, P. M., Nguyen, H. T., Bach, N. X., Tran, V., Le Nguyen, M., & Phuong, T. M. (2020, December). Answering legal questions by learning neural attentive text representation. In Proceedings of the 28th International Conference on Computational Linguistics (pp. 988-998). Search in Google Scholar