Applying decision tree analysis to family court decisions

Huang, Sieh-Chuen; Shao, Hsuan-Lei; Leflar, Robert B

doi:10.1145/3462757.3466076

Cited by 2 publications

(1 citation statement)

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“…The majority of the current work is about criminal cases, and the goals are to predict the penalties against the defendants. So far, work on civil cases like [6] is relatively uncommon. When addressing the applications of machine learning techniques to the prediction tasks, Mumford et al [9] commented that "without an explanation of why the case was so classified, the adjudicator has no reason to follow.…”

Section: Introductionmentioning

confidence: 99%

Functional Classification of Statements of Chinese Judgment Documents of Civil Cases

Liu

Lin

Liu

et al. 2022

Frontiers in Artificial Intelligence and Applications

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Enabling the inference systems for assisting legal decisions to identify the functions of sentences and paragraphs in documents of legal judgments can enhance the justifiability of their algorithmic recommendations. The information about the functions of larger linguistic constituents complements the information at the word level like NER, and provides more clues about the arguments for the legal decisions. We explore this venue for the civil cases, which is a relatively uncommon choice in legal informatics and more challenging than working on the criminal cases. Current experimental results are promising.

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Section: Introductionmentioning

confidence: 99%

Functional Classification of Statements of Chinese Judgment Documents of Civil Cases

Liu

Lin

Liu

et al. 2022

Frontiers in Artificial Intelligence and Applications

View full text Add to dashboard Cite

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A neural network to identify requests, decisions, and arguments in court rulings on custody

Muñoz-Soro,

del Hoyo Alonso,

Montañes

et al. 2024

Artif Intell Law

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Court rulings are among the most important documents in all legal systems. This article describes a study in which natural language processing is used for the automatic characterization of Spanish judgments that deal with the physical custody (joint or individual) of minors. The model was trained to identify a set of elements: the type of custody requested by the plaintiff, the type of custody decided on by the court, and eight of the most commonly used arguments in this type of judgment. Two jurists independently annotated more than 3000 judgments, which were used to train a model based on transformers. The main difficulties encountered in this task were the complexity of the judicial language and the need to work with appellate court rulings that have a more complicated structure than decisions at first instance. For the complete court rulings, the F1 score of the inter-annotator agreement ranged from 0.60 to 0.86 and the Kappa index from 0.33 to 0.73. The F1 score of the agreement between the model and the annotators ranged from 0.66 to 0.93 and the Kappa index from 0.57 to 0.80. These results in which the model performance exceeds even the inter-annotator agreement show the high ability of transformers to identify abstract entities in legal texts.

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Applying decision tree analysis to family court decisions

Cited by 2 publications

References 0 publications

Functional Classification of Statements of Chinese Judgment Documents of Civil Cases

Functional Classification of Statements of Chinese Judgment Documents of Civil Cases

A neural network to identify requests, decisions, and arguments in court rulings on custody

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