Research and Design on Cognitive Computing Framework for Predicting Judicial Decisions (original) (raw)
References
Lin, W.-C., Kuo, T.-T., Chang, T.-J. (2012). Exploiting machine learning models for Chinese legal documents labeling, case classification, and sentencing prediction. ROCLING XXIV (pp. 140).
Liu, Y.-H., Chen, Y.-L., Ho, W.-L. (2015). Predicting associated statutes for legal problems. Information Processing and Management, 51(1), 194C211. Article Google Scholar
Aletras, N., Tsarapatsanis, D., Preotiuc-Pietro, D., Lampos, V. (2016). Predicting judicial decisions of the European court of human rights: A natural language processing perspective. PeerJ Computer Science, 2, e93. Article Google Scholar
Luo, B., Feng, Y., Xu, J., Zhang, X., Zhao, D. (2017). Learning to predict charges for criminal cases with legal basis. Conference on Empirical Methods in Natural Language Processing (pp. 2727–2736).
Kelly, I. E. (2015). Computing cognition and the future of knowing. IBM Research: Cognitive Computing. IBM Corporation.
Ludwig, L. (2013). Extended Artificial Memory. Toward an integral cognitive theory of memory and technology. Technische University Kaiserslautern.
Ferrucci, D.A. (2012). Introduction to ”this is watson”. IBM Journal of Research and Development, 56(3), 1. Google Scholar
Chen, Y.-L., Liu, Y.-H., Ho, W.-L. (2013). A text mining approach to assist the general public in the retrieval of legal documents. JASIST, 64(2), 280C290. Google Scholar
Raghav, K., Reddy, P.B., Reddy, V.B., Reddy, P.K. (2015). Text and citations based cluster analysis of legal judgments. In MIKE (pp. 449C459). Springer.
Raghav, K., Reddy, P.K., Reddy, V.B. (2016). Analyzing the extraction of relevant legal judgments using paragraph-level and citation information. AI4JCArtificial Intelligence for Justice (pp. 30).
Kim, M.-Y., Xu, Y., Goebel, R. (2014). Legal question answering using ranking svm and syntactic/semantic similarity. In JSAI International Symposium on Artificial Intelligence (pp. 244C258). Springer.
Carvalho, D., Nguyen, M.-T., Tran, C.-X., Nguyen, M.-L. (2016). Lexical-morphological modeling for legal text analysis. New Frontiers in Artificial Intelligence (Lecture Notes in Artificial Intelligence), 10091. https://doi.org/10.1007/978-3-319-50953-2. Google Scholar
Liu, C.-L., & Liao, T.-M. (2005). Classifying criminal charges in chinese for web-based legal services. In Asia-PacificWeb Conference (pp. 64C75), Springer.
Liu, C.-L., Chang, C.-T., Ho, J.-H. (2004). Case instance generation and refinement for case-based criminal summary judgments in chinese. Journal of Information Science and Engineering, 20(4), 783C800. Google Scholar
Liu, C.-L., & Hsieh, C.-D. (2006). Exploring phrase-based classification of judicial documents for criminal charges in chinese. In International Symposium on Methodologies for Intelligent Systems (pp. 681C690). Springer.
Sim, Y, Rouledge, B, Smith, NA. (2014). The Utility of Text: The Case of Amicus Briefs and the Supreme Court. Eprint Arxiv.
Katz, D.M., Bommarito, I.I., Michael, J., Blackman, J. (2016). A general approach for predicting the behavior of the supreme court of the United States. arXiv:1612.03473.
Li, J., Li, X., Meng, T. (2015). TML: A General High-performance Text Mining Language[J]. Computer Research and Development, 52(3), 553–560. Google Scholar
Mikolov, T, Chen, K, Corrado, G, Dean, J. (2013). Efficient Estimation of Word Representations in Vector Space[J]. Computer Science.
Zhai, F, Potdar, S, Xiang, B, Zhou, B. (2017). Neural Models for Sequence Chunking. AAAI.
Ma, X., & Hovy, E. (2016). End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF [J]. ACL.
Zeng, D.J., Liu, K., Lai, S.W., Zhou, G., Zhao, J. (2014). Relation classification via convolutional deep neural network. In The 25th international conference on computational linguistics (COLING2014) (pp. 2335–2344).
Singla, P., & Domingos, P. Discriminative training of Markov logic networks. In National Conference on Artificial Intelligence (pp. 868–873). AAAI.