Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review

Keeling, Robert; Chhatwal, Rishi; Huber-Fliflet, Nathaniel; Zhang, Jianping; Wei, Fusheng; Zhao, Haixing; Ye, Shi; Qin, Han

doi:10.1109/bigdata47090.2019.9006248

Cited by 23 publications

(11 citation statements)

References 13 publications

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“…TextCNN produces good results in short text classification. Other experiments [48] show that when the text length exceeds 2000, the performance of TextCNN is not significantly improved compared with the traditional methods. TextRNN [49] is a well-known text classification model; however, it is not effective at handling long documents because a long text leads to a vanishing gradient.…”

Section: Deep Learning-based Methodsmentioning

confidence: 97%

Mining the Frequent Patterns of Named Entities for Long Document Classification

Wang

Gao

et al. 2022

Applied Sciences

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Nowadays, a large amount of information is stored as text, and numerous text mining techniques have been developed for various applications, such as event detection, news topic classification, public opinion detection, and sentiment analysis. Although significant progress has been achieved for short text classification, document-level text classification requires further exploration. Long documents always contain irrelevant noisy information that shelters the prominence of indicative features, limiting the interpretability of classification results. To alleviate this problem, a model called MIPELD (mining the frequent pattern of a named entity for long document classification) for long document classification is demonstrated, which mines the frequent patterns of named entities as features. Discovered patterns allow semantic generalization among documents and provide clues for verifying the results. Experiments on several datasets resulted in good accuracy and marco-F1 values, meeting the requirements for practical application. Further analysis validated the effectiveness of MIPELD in mining interpretable information in text classification.

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Section: Deep Learning-based Methodsmentioning

confidence: 97%

Mining the Frequent Patterns of Named Entities for Long Document Classification

Wang

Gao

et al. 2022

Applied Sciences

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“…However, it has recently been applied to natural language processing problems and has given very good results. In addition to the two layers input and output, CNN network architecture is composed of several types of layers including [13,14]. Recurrent Neural Network (RNN) is an algorithm that has received a lot of attention recently because of its good results obtained in the field of natural language processing [15]: − using the software reuse method: we reuse some available software in Vietnamese document processing such as data cleaning, word separation, vectorization in the process of building a tool to detect fake speech freeforms.…”

Section: Literature Review and Problem Statementmentioning

confidence: 99%

Development of a fake news detection tool for Vietnamese based on deep learning techniques

Phan

Ninh

2022

EEJET

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With the development of the Internet, social networks and different communication channels, people can get information quickly and easily. However, in addition to real and useful news, we also receive false and unreal information. The problem of fake news has become a difficult and unresolved issue. For languages with few users, such as Vietnamese, the research on fake news detection is still very limited and has not received much attention. In this paper, we present research results on building a tool to support fake news detection for Vietnamese. Our idea is to apply text classification techniques to fake news detection. We have built a database of 4 groups of 2 topics about politics (fake news and real news) and about Covid-19 (fake news and real news). Then use deep learning techniques CNN (Convolutional Neural Network) and RNN (Recurrent Neural Network) to create the corresponding models. When there is new news that needs to be verified, we just need to apply the classification to see which of the four groups they label into to decide whether it is fake news or not. The tool was able to detect fake news quickly and easily with a correct rate of about 85 %. This result will be improved when getting a larger training data set and adjusting the parameters for the machine learning model. These results make an important contribution to the research on detecting fake news for Vietnamese and can be applied to other languages. In the future, besides using classification techniques (based on content analysis), we can combine many other methods such as checking the source, verifying the author's information, checking the distribution process to improve the quality of fake news detection.

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“…With the rapid development of artificial intelligence technology, machine learning, especially deep learning, have become a new trend in the development of intelligent judicial technology in the judicial field. Robert et al use convolutional neural networks (CNNs) to extract semantic features from case text [13], thus supporting the deep mining of case data. Although CNNs can perform well in many tasks, CNN models cannot model case summary information accurately, thus failing to achieve the case grouping encoding required in this paper.…”

Section: Related Workmentioning

confidence: 99%

An Improved AdaBoost for Prosecutorial Case-Workload Estimation via Case Grouping

Min

Yang

et al. 2022

Int J Comput Intell Syst

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Case-workload estimation has always been a complex process and plays a vital role in prosecutorial work. Despite the increasing development of rule-based techniques, artificial intelligence and machine learning have rarely been used to study case-workload estimation problems, leaving many cases processed without quantitative estimation. This paper aims to develop a new case-work estimation method that combines artificial intelligence methods with practical needs and apply it to the case assignment system of the prosecutor’s office. We propose a feature learning model, the improved AdaBoost model, to capture the features of cases for case grouping to estimate case workload. We first learn the case textual data based on the judicial proper noun dictionary, extract the case labels from the case information with the AdaBoost learner, and group and encode each case by fuzzy matching. Then, the extracted vital information estimates case workload based on the length of case processing time and suspects number, respectively. We conducted extensive experiments to compare the proposed method with eight baseline methods, including the traditional AdaBoost classifier, to evaluate the performance of the proposed model on a real prosecution case dataset. The experimental results demonstrate the superiority of our proposed workload estimation model.

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Empirical Comparisons of CNN with Other Learning Algorithms for Text Classification in Legal Document Review

Cited by 23 publications

References 13 publications

Mining the Frequent Patterns of Named Entities for Long Document Classification

Mining the Frequent Patterns of Named Entities for Long Document Classification

Development of a fake news detection tool for Vietnamese based on deep learning techniques

An Improved AdaBoost for Prosecutorial Case-Workload Estimation via Case Grouping

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