Legal Judgment Prediction (LJP) focuses on predicting judgment results based on the facts of cases. While State-of-the-Art (SOTA) methods have shown impressive performance in law article prediction and charge prediction, they still exhibit weaknesses in prison term prediction. One major reason is that existing models fail to mimic human legal quantitative reasoning to understand monetary features in case facts. Consequently, they do not rigorously quantify the severity of the crime, which is essential for prison term prediction. In this article, we explore and explain how to leverage monetary features to improve LJP via quantitative reasoning. Specifically, we propose QR-LJP, a quantitative reasoning-based LJP model, to integrate legal reasoning knowledge into the prediction process. QR-LJP first employs a curated LLM to extract monetary values from case facts and uses legal quantitative reasoning logic to determine the total crime amount, serving as the quantitative measure of the crime’s severity. This measure is subsequently used to make judgment predictions. We evaluate our model on the real-world dataset CAIL-2018. Experimental results demonstrate that our model outperforms current SOTAs, highlighting the effectiveness of legal quantitative reasoning. Moreover, applying our quantitative reasoning strategy to existing SOTA methods yields significant improvements, especially in macro-F1 scores.

Bug localization is the task of recommending source code locations (typically files) that probably contain the cause of a bug and hence need to be changed to fix the bug. Along these lines, information retrieval-based bug localization (IRBL) approaches have been adopted, which identify the most bug-prone files from the source code space. In current practice, a series of state-of-the-art IRBL techniques leverage the combination of different components, e.g., similar reports, version history, code structure, to achieve better performance. ABLoTS is a recently proposed approach with the core component, TraceScore, that utilizes requirements and traceability information between different issue reports, i.e., feature requests and bug reports, to identify buggy source code snippets with promising results. To evaluate the accuracy of these results and obtain additional insights into the practical applicability of ABLoTS, supporting of future more efficient and rapid replication and comparison, we conducted a replication study of this approach with the original data set and also on an extended data set. The extended data set includes 16 more projects comprising 25,893 bug reports and corresponding source code commits. While we find that the TraceScore component as the core of ABLoTS produces comparable results with the extended data set, we also find that the ABLoTS approach no longer achieves promising results, due to an overlooked side effect of incorrectly choosing a cut-off date that led to training data leaking into test data with significant effects on performance.

In the continental law system, it is appropriate for judges to find relevant laws and consider rules defined in them when dealing with legal cases. Therefore, recommending relevant laws quickly and accurately based on case content is crucial in improving the efficiency of case processing. There have been researched works of recommender systems in various fields, but few of them lucubrates systems that recommend statutes for cases. To the best of our knowledge, there is no research on recommending statutes by modeling the relationship between case content and law content with interpretable hand-crafted features. In this paper, we define five novel types of features for calculating relevance between a case and a statute for resorting all statutes retrieved through collaborative filtering for the input case. Both pair-wise and list-wise ranking models are trained based on all these features for re-ranking the statutes list. Besides, we also test the combinations of different learning algorithms and popular pre-trained language models. Experimental results show that adopting the proposed novel features in pair-wise ranking achieves the best performance. It improves the recommendation recall of the Top 1 statute by almost 5% compared with the collaborative filtering approach.

Legal judgment prediction, which aims at predicting judgment results such as penalty, charges, and statutes for cases, has attracted much attention recently. In this article, we focus on building a recommender system to predict the associated statutes for a case given the facts of the case as input. For this purpose, we propose a two-step neural network-based machine learning framework to assist judges as well as ordinary people to reduce their effort in finding applicable statutes. The proposed model takes advantage of recurrent neural networks with a max-pooling layer to obtain contextual representations of documents, i.e., the facts associated with the cases. Moreover, an attention mechanism is used to automatically focus on the important words contributing to the prediction of statutes. In addition, we apply an encoder--decoder ranking approach to extract correlations between statutes to achieve more accurate recommendation results. We evaluate our model on a real-world dataset. Experimental results show that, compared with existing baseline methods, our method can predict statutes that are more likely to appear in real judgments.

In the case of a traditional court judge, the facts are based on the law as the cornerstone, the fact that can be proved by the legal evidences. As we all know, assisting judges to manage evidence chain information can significantly improve the efficiency and quality of judges. Therefore, based on this idea, this paper will introduce the design and implementation of Visual Modeling Tool for evidence chain. The tool can help the judge to build various types of evidence chain, and can help to improve the work efficiency of judges. This visual modeling tool is divided into two main forms of visualization, includes the Graphical Mode and Table Mode. It means the same data with different display forms. So that the judge can deal with a large number of complex and varied evidence of chain information quickly and easily. Also, the efficiency of the judge to handle the case can be significantly improved.

This paper presents a method of the association statistics between the cause of action and the statute. According to the close relationship between the cause of action and the statute in the written judgment, this paper puts forward the statistical analysis of the cause of action and the statute. The method mainly includes the pretreatment of semi-structured written judgments, reading information of the cause of action and the statute from structured documents, standardizing statutes, depositing in the database, generating EXCEL form of the association statistics from the cause of action to the statue and generating TXT form of the association statistics from the statue to the cause of action. In the process of reasoning and assessment, we can achieve the prediction of statutes and narrow the size of the cause of action.

With the continuous advancement of the informatization of the Chinese People's Court, the court's view on the extraction and application of information has not only been on the structured data, but also for the semi-structured and unstructured data. In the process of in-depth study of the judgment document, many cases require the collection of the document result as an important data dimension, and the key is that the statute is the core of the whole result, so the integrity and correctness of the statute obtained has played a key role for the process of the judgment document processing. However, in the process of writing a specific judgment document, the same statute has different string forms due to the diversity of writing, which leads directly to the error data source. Comparing the editing distance between the strings can judge the similarity of them to a certain extent. Therefore, an automatic method based on the editing distance algorithm is devised, which constructs the disparity model between different statutes strings, to obtain the standardized writing of the same type data. Using this method can remove the non-standard writing of statutes, and ultimately access to the standard statutes collection. This method has a higher efficiency than the method of enumerating all the writing circumstances, which needs the manual participation, additional data storage and update.

The traditional approach to measure text similarity is based on the TF-IDF algorithm to get the document vector, and then use the cosine similarity algorithm to calculate the text similarity. However, this method of statistical way ignores the potential semantics of the articles or words. By some means, this method only aims at the word itself. But with the Latent Semantic Analysis, the semantic space is added on the basis of calculate TF-IDF. Each word and document can have a position in semantic space by Singular Value Decomposition. That allows the semantic analysis, document clustering, and the relationship between semantic class and document class can be finished at the same time. Here, we summarize the text similarity measures, and gradually extend to the Latent Semantic Analysis. The experiment shows that the statutes predicted by LSA are more accurate than that only by TF-IDF.

Judgment documents contain a wealth of valuable information. The original judgment documents are written in pure text format, so we cannot obtain information directly, which hinders the study of the judgment documents. We propose an approach to parse Chinese judgment documents into structured documents to solve this problem. Divide a judgment document into logical segments, and then extract and label information items from these logical segments. Use information items to build analytic document information model and the model is output into a structured XML document.