5,287 research outputs found
Court Judgment Decision Support System Based on Medical Text Mining
Medical damage is a common problem faced by hospitals around the world and is widely watched by countries and the World Health Organization. As the number of medical damage dispute lawsuit cases rapidly grows, many countries in the world face the problem how to improve the efficiency of the judicial system under the premise of guaranteeing the quality of the trial. Therefore, in addition to reforming the system, the decision support system will effectively improve judicial decisions. This paper takes medical damage judgment documents in China as example, and proposes a court judgment decision support system (CJ-DSS) based on medical text mining and the automatic classification technology. The system can predict the trail results of the new lawsuit documents according to the previous cases verdict - rejected and non-rejected. Combined with the cases, the study in this paper found that combined feature extraction method does improve the performance of three kinds of classifiers - Support Value Machine (SVM), Artificial Neural Network (ANN) and K-Nearest Neighbor (KNN), the degree of improved performance is different from using DF-CHI combined feature extraction method. In addition, integrated learning algorithm also improves the classification performance of the overall system
Revisiting Pre-Trained Models for Chinese Natural Language Processing
Bidirectional Encoder Representations from Transformers (BERT) has shown
marvelous improvements across various NLP tasks, and consecutive variants have
been proposed to further improve the performance of the pre-trained language
models. In this paper, we target on revisiting Chinese pre-trained language
models to examine their effectiveness in a non-English language and release the
Chinese pre-trained language model series to the community. We also propose a
simple but effective model called MacBERT, which improves upon RoBERTa in
several ways, especially the masking strategy that adopts MLM as correction
(Mac). We carried out extensive experiments on eight Chinese NLP tasks to
revisit the existing pre-trained language models as well as the proposed
MacBERT. Experimental results show that MacBERT could achieve state-of-the-art
performances on many NLP tasks, and we also ablate details with several
findings that may help future research. Resources available:
https://github.com/ymcui/MacBERTComment: 12 pages, to appear at Findings of EMNLP 202
Automatic Extraction of Theft Judgment Information in Natural Language
Recently artificial intelligence technology replaces traditional manual methods in many fields. Especially the application of artificial intelligence in the legal field liberates legal people from tedious work. For example, crimes are automatically classified based on the facts of the crime, such as the crime name and sentence prediction. However, the premise of these applications is based on the establishment of case bases. Therefore, this paper studies the issue of automatic extraction of verdict information in natural language. Due to the verbal writing specification, we use regular expressions to construct extraction rule templates for template matching. At the same time, we also use natural language processing technology to extract the relevant semantic information accurately. For further similar case searching. The research focus of this paper is on the theft verdicts, and establish a database of records for the theft of the theft and the prediction of the theft of the theft
Learning to Predict Charges for Criminal Cases with Legal Basis
The charge prediction task is to determine appropriate charges for a given
case, which is helpful for legal assistant systems where the user input is fact
description. We argue that relevant law articles play an important role in this
task, and therefore propose an attention-based neural network method to jointly
model the charge prediction task and the relevant article extraction task in a
unified framework. The experimental results show that, besides providing legal
basis, the relevant articles can also clearly improve the charge prediction
results, and our full model can effectively predict appropriate charges for
cases with different expression styles.Comment: 10 pages, accepted by EMNLP 201
SECaps: A Sequence Enhanced Capsule Model for Charge Prediction
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 a attention residual unit, which provides
auxiliary information for charge prediction. In addition, our SECaps model
introduces focal loss, which relieves the problem of imbalanced charges.
Comparing the state-of-the-art methods, our 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 our proposed model.Comment: 13 pages, 3figures, 5 table
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