Clustering of Brazilian legal judgments about failures in air transport service: an evaluation of different approaches

Sabo, Isabela Cristina; Pont, Thiago Raulino Dal; Wilton, Pablo Ernesto Vigneaux; Rover, Aires José; Hübner, Jomi Fred

doi:10.1007/s10506-021-09287-3

Cited by 7 publications

(5 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To construct the dataset, a legal expert collected all the documents to avoid repeated judgments or judgments about a subject not related to failures in air transport service. The legal expert also manually extracted some attributes and their values from each document, which was possible through a clustering step ( Sabo et al, 2021 ). One of the attributes identified, for example, is the flight delay period.…”

Section: Dataset Context and Constructionmentioning

confidence: 99%

Regression applied to legal judgments to predict compensation for immaterial damage

Pont

Sabo

Hübner

et al. 2023

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

Immaterial damage compensation is a controversial matter in the judicial practice of several law systems. Due to a lack of criteria for its assessment, the judge is free to establish the value based on his/her conviction. Our research motivation is that knowing the estimated amount of immaterial damage compensation at the initial stage of a lawsuit can encourage an agreement between the parties. We thus investigate text regression techniques to predict the compensation value from legal judgments in which consumers had problems with airlines and claim for immaterial damage. We start from a simple pipeline and create others by adding some natural language processing (NLP) and machine learning (ML) techniques, which we call adjustments. The adjustments include N-Grams Extraction, Feature Selection, Overfitting Avoidance, Cross-Validation and Outliers Removal. An special adjustment, Addition of Attributes Extracted by the Legal Expert (AELE), is proposed as a complementary input to the case text. We evaluate the impact of adding these adjustments in the pipeline in terms of prediction quality and execution time. N-Grams Extraction and Addition of AELE have the biggest impact on the prediction quality. In terms of execution time, Feature Selection and Overfitting Avoidance have significant importance. Moreover, we notice the existence of pipelines with subsets of adjustments that achieved better prediction quality than a pipeline with them all. The result is promising since the prediction error of the best pipeline is acceptable in the legal environment. Consequently, the predictions will likely be helpful in a legal environment.

show abstract

Section: Dataset Context and Constructionmentioning

confidence: 99%

Regression applied to legal judgments to predict compensation for immaterial damage

Pont

Sabo

Hübner

et al. 2023

PeerJ Computer Science

Self Cite

View full text Add to dashboard Cite

show abstract

“…Supervised and unsupervised models are frequently employed in the judicial area. Sabo et al (2021) used clustering approaches in judicial decisions to obtain information about important factors in the air transport, such as service failures. Lei et al (2017) used supervised models: Naive Bayes, Support Vector Machines and Random Forest to classify documents of Chinese court decisions.…”

Section: Importance Of Machine Learning Approaches In Different Areas...mentioning

confidence: 99%

“…If customer assistance is not provided, it may still be recourse to the special civil courts in some Brazilian airports. According to Sabo et al (2021), these courts were created to enable citizens' access to justice. Another way to solve conflicts is to contact the company directly through the Consumidor.gov platform, a public service to solve consumer-company disputes considering lawsuits the last measure.…”

mentioning

confidence: 99%

See 1 more Smart Citation

Legal actions in Brazilian air transport: A machine learning and multinomial logistic regression analysis

Torres

Guterres

Celestino

2023

Front. Future Transp.

View full text Add to dashboard Cite

In Brazil, one of the most harmful costs for airlines is the number of lawsuits filed against them. It is a problem that can affect its operations, reduce the entry of new competitors and create legal uncertainty in the country. This work seeks to highlight the factors which most contribute to the rise of judicial indemnities, discuss the most relevant issues and identify the best techniques to predict the indemnified values. The objective is to provide subsidies for airlines to mitigate the number of legal actions by using machine learning models. This research contributes by discussing one of the most relevant subjects in Brazilian air transport and comparing the machine learning models’ performance. The study is based on lawsuits between 2016 and 2021 using the companies’ data. The performance of Naive Bayes, Random Forest, Support Vector Machines, and Multinomial Logistic Regression models are evaluated through the accuracy, area under the ROC curve, and confusion matrix. The results showed better predictive power for Random Forest and Logistic Regression. The latter showed that flight delays, cancellations, and airline faults have a negative effect on indemnities. The above-average compensation is a tendency in some states, being the moral damage awarded to customers the main cause of higher compensation.

show abstract

“…Though these speeches concern legal topics and contain legal terms, they are textually shorter and less technical than most legal documents. More recently, a team of Brazilian researchers explored various LDA techniques for dense and highly-technical court judgments (Sabo et al, 2022). They discovered that all four of their LDA techniques—Hierarchical, Lingo, K-Means and Affinity Propagation–revealed patterns in the court documents, but that Hierarchical clustering was particularly suitable to the features of Brazilian court opinions (Sabo et al, 2022).…”

Section: Literature Reviewmentioning

confidence: 99%

The law and policy of library development: A legal text analysis of Indonesian library bills

Ali,

Ryan

2023

Alexandria: The Journal of National and International Library a

View full text Add to dashboard Cite

The Indonesian constitution provides for welfare and education. To achieve these constitutional mandates, the Indonesian government passed several laws as the foundation for lifelong education, including the Library Act of 2007 and the National Repository Act of 2018. These Acts are the main legal foundation for the National Library of Indonesia (NLI) to create public policies in library service development across Indonesia. This study seeks to understand these laws through Natural Language Processing (NLP) and Latent Dirichlet allocation (LDA) Topic Modelling. Using these methods, we uncovered the major themes of these legal mandates for library services. Our research revealed five topics for each law. We then analyzed the NLI budgets from 2021 to 2023 to see how the key legal mandates were implemented. We found greater implementation of the 2007 Act than the 2018 Act. Our study offers new methods for library law and policy research.

show abstract

Clustering of Brazilian legal judgments about failures in air transport service: an evaluation of different approaches

Cited by 7 publications

References 23 publications

Regression applied to legal judgments to predict compensation for immaterial damage

Regression applied to legal judgments to predict compensation for immaterial damage

Legal actions in Brazilian air transport: A machine learning and multinomial logistic regression analysis

The law and policy of library development: A legal text analysis of Indonesian library bills

Contact Info

Product

Resources

About