Automatic charge prediction aims to predict appropriate final charges according to the fact descriptions for a given criminal case. Automatic charge prediction plays a critical role in assisting judges and lawyers to improve the efficiency of legal decisions, and thus has received much attention. Nevertheless, most existing works on automatic charge prediction perform adequately on high-frequency charges but are not yet capable of predicting few-shot charges with limited cases. In this paper, we propose a Sequence Enhanced Capsule model, dubbed as SECaps model, to relieve this problem. Specifically, following the work of capsule networks, we propose the seq-caps layer, which considers sequence information and spatial information of legal texts simultaneously. Then we design an attention residual unit, which provides auxiliary information for charge prediction. In addition, SECaps model introduces focal loss, which relieves the problem of imbalanced charges. Comparing the state-of-the-art methods, SECaps model obtains 4.5 and 6.4 absolutely considerable improvements under Macro F1 in Criminal-S and Criminal-L respectively. The experimental results consistently demonstrate the superiorities and competitiveness of SECaps model.
Researchers begin to use machine learning methods to handle charge prediction because of its success in many areas. This type of work usually focuses on extracting features from case facts and then using machine learning algorithms to make predictions. @cite_26 @cite_35 use k-Nearest Neighbor (KNN) method to classify criminal charges. @cite_10 fetches 21 legal factor labels for case classification. @cite_38 extracts N-grams features which creates by clustering semantically similar N-grams. @cite_5 propose a SVM-based system, which uses the case description, time span and ruling as features. However, these methods only extract shallow text features or manual tags, which are difficult to collect on larger datasets. Therefore, when the amount of the data is large, they will not perform well.
@cite_35 @cite_38 @cite_26 @cite_5 @cite_10
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Phrases provide a better foundation for indexing and retrieving documents than individual words. Constituents of phrases make other component words in the phrase less ambiguous than when the words appear separately. Intuitively, classifiers that employ phrases for indexing should perform better than those that use words. Although pioneers have explored the possibility of indexing English documents decades ago, there are relatively fewer similar attempts for Chinese documents, partially because segmenting Chinese text into words correctly is not easy already. We build a domain dependent word list with the help of Chien's PAT tree-based method and HowNet, and use the resulting word list for defining relevant phrases for classifying Chinese judicial documents. Experimental results indicate that using phrases for indexing indeed allows us to classify judicial documents that are closely similar to each other. With a relatively more efficient algorithm, our classifier offers better performances than those reported in related works. Researchers have applied case-based reasoning to legal informatics in recent decades, yet real-world applications of case-based systems have been hampered by the need to manually create the case instances. To alleviate this problem, we propose algorithms for automatically generating and refining case instances from real-world judgment documents for criminal summary judgments. Our algorithms attempt to extract important legal information from the documents of past lawsuits to build case instances, and then refine these case instances by merging similar cases and removing relatively irrelevant information from the cases. Experimental results, obtained from applying the resulting cases to classifying real-world lawsuits, indicate that our algorithms are viable for assisting the task of building case databases for case-based legal reasoning systems. 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. This paper exploits machine learning methods to separate robbery and intimidation cases, and predicting their sentencing by considering defined legal factors. We introduce a framework to fetch 21 legal factor labels of robbery and intimidation cases, then use the labels for case classification and sentencing prediction. Our experiments show that the legal factor labels can indeed improve the results of case classification and sentencing prediction. We then discuss the influence of these legal factors in both case classification and sentencing prediction tasks.