In this paper, we investigate the application of text classification methods to support law professionals. We present several experiments applying machine learning techniques to predict with high accuracy the ruling of the French Supreme Court and the law area to which a case belongs to. We also investigate the influence of the time period in which a ruling was made on the form of the case description and the extent to which we need to mask information in a full case ruling to automatically obtain training and test data that resembles case descriptions. We developed a mean probability ensemble system combining the output of multiple SVM classifiers. We report results of 98 average F1 score in predicting a case ruling, 96 F1 score for predicting the law area of a case, and 87.07 F1 score on estimating the date of a ruling.
@cite_6 proposed a system of classifying sentences for the task of summarizing court rulings and, with the use of SVM and Naive Bayes applied to Bag of Words, TF-IDF, and dense features (e.g. position of sentence in document), obtained 65 For court ruling prediction, the task closest to our present work, a few papers have been published: @cite_22 , using extremely randomized trees, reported 70 As evidenced in this section predicting court rulings is a new area for text classification methods and our paper contributes in this direction, achieving performance substantially higher than in previous work @cite_14 .
@cite_14 @cite_22 @cite_6
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In this paper, we investigate the application of text classification methods to predict the law area and the decision of cases judged by the French Supreme Court. We also investigate the influence of the time period in which a ruling was made over the textual form of the case description and the extent to which it is necessary to mask the judge's motivation for a ruling to emulate a real-world test scenario. We report results of 96 f1 score in predicting a case ruling, 90 f1 score in predicting the law area of a case, and 75.9 f1 score in estimating the time span when a ruling has been issued using a linear Support Vector Machine (SVM) classifier trained on lexical features. Building upon developments in theoretical and applied machine learning, as well as the efforts of various scholars including Guimera and Sales-Pardo (2011), (2004), and (2004), we construct a model designed to predict the voting behavior of the Supreme Court of the United States. Using the extremely randomized tree method first proposed in Geurts, et al (2006), a method similar to the random forest approach developed in Breiman (2001), as well as novel feature engineering, we predict more than sixty years of decisions by the Supreme Court of the United States (1953-2013). Using only data available prior to the date of decision, our model correctly identifies 69.7 of the Court’s overall affirm reverse decisions and correctly forecasts 70.9 of the votes of individual justices across 7,700 cases and more than 68,000 justice votes. Our performance is consistent with the general level of prediction offered by prior scholars. However, our model is distinctive as it is the first robust, generalized, and fully predictive model of Supreme Court voting behavior offered to date. Our model predicts six decades of behavior of thirty Justices appointed by thirteen Presidents. With a more sound methodological foundation, our results represent a major advance for the science of quantitative legal prediction and portend a range of other potential applications, such as those described in Katz (2013). We describe research carried out as part of a text summarisation project for the legal domain for which we use a new XML corpus of judgments of the UK House of Lords. These judgments represent a particularly important part of public discourse due to the role that precedents play in English law. We present experimental results using a range of features and machine learning techniques for the task of predicting the rhetorical status of sentences and for the task of selecting the most summary-worthy sentences from a document. Results for these components are encouraging as they achieve state-of-the-art accuracy using robust, automatically generated cue phrase information. Sample output from the system illustrates the potential of summarisation technology for legal information management systems and highlights the utility of our rhetorical annotation scheme as a model of legal discourse, which provides a clear means for structuring summaries and tailoring them to different types of users.