Explainable machine learning multi-label classification of Spanish legal judgements

Arriba-Pérez, Francisco de; García-Méndez, Silvia; González-Castaño, Francisco J.; González-González, Jaime

doi:10.1016/j.jksuci.2022.10.015

Cited by 4 publications

(1 citation statement)

References 55 publications

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“…However, to our knowledge, no previous research has applied xai techniques to legal judgment classification approaches, that is, to automatically explain their decisions in natural language so that they are understandable to a person, the only exception being our previous research on multi-label legal text classification (Arriba-Pérez et al, 2022). From these promising previous results, this paper focuses on the variation of law categories depending on legal jurisdictions.…”

Section: Related Workmentioning

confidence: 99%

Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators

González-González,

de Arriba-Pérez,

García-Méndez

et al. 2023

Journal of King Saud University - Computer and Information Scie

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Section: Related Workmentioning

confidence: 99%

Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators

González-González,

de Arriba-Pérez,

García-Méndez

et al. 2023

Journal of King Saud University - Computer and Information Scie

Self Cite

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Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models

de Arriba-Pérez,

García-Méndez,

Otero-Mosquera

et al. 2024

Appl Intell

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Cognitive and neurological impairments are very common, but only a small proportion of affected individuals are diagnosed and treated, partly because of the high costs associated with frequent screening. Detecting pre-illness stages and analyzing the progression of neurological disorders through effective and efficient intelligent systems can be beneficial for timely diagnosis and early intervention. We propose using Large Language Models to extract features from free dialogues to detect cognitive decline. These features comprise high-level reasoning content-independent features (such as comprehension, decreased awareness, increased distraction, and memory problems). Our solution comprises (i) preprocessing, (ii) feature engineering via Natural Language Processing techniques and prompt engineering, (iii) feature analysis and selection to optimize performance, and (iv) classification, supported by automatic explainability. We also explore how to improve Chatgpt’s direct cognitive impairment prediction capabilities using the best features in our models. Evaluation metrics obtained endorse the effectiveness of a mixed approach combining feature extraction with Chatgpt and a specialized Machine Learning model to detect cognitive decline within free-form conversational dialogues with older adults. Ultimately, our work may facilitate the development of an inexpensive, non-invasive, and rapid means of detecting and explaining cognitive decline.

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Legal Natural Language Processing From 2015 to 2022: A Comprehensive Systematic Mapping Study of Advances and Applications

Quevedo,

Cerny,

Rodriguez

et al. 2024

IEEE Access

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The surge in legal text production has amplified the workload for legal professionals, making many tasks repetitive and time-consuming. Furthermore, the complexity and specialized language of legal documents pose challenges not just for those in the legal domain but also for the general public. This emphasizes the potential role and impact of Legal Natural Language Processing (Legal NLP). Although advancements have been made in this domain, particularly after 2015 with the advent of Deep Learning and Large Language Models (LLMs), a systematic exploration of this progress until 2022 is nonexistent. In this research, we perform a Systematic Mapping Study (SMS) to bridge this gap. We aim to provide a descriptive statistical analysis of the Legal NLP research between 2015 and 2022. Categorize and sub-categorize primary publications based on their research problems. Identify limitations and areas of improvement in current research. Using a robust search methodology across four reputable indexers, we filtered 536 papers down to 75 pivotal articles. Our findings reveal the diverse methods employed for tasks such as Multiclass Classification, Summarization, and Question Answering in the Legal NLP field. We also highlight resources, challenges, and gaps in current methodologies and emphasize the need for curated datasets, ontologies, and a focus on inherent difficulties like data accessibility. As the legal sector gradually embraces Natural Language Processing (NLP), understanding the capabilities and limitations of Legal NLP becomes vital for ensuring efficient and ethical application. The research offers insights for both Legal NLP researchers and the broader legal community, advocating for continued advancements in automation while also addressing ethical concerns.

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Explainable machine learning multi-label classification of Spanish legal judgements

Cited by 4 publications

References 55 publications

Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators

Automatic explanation of the classification of Spanish legal judgments in jurisdiction-dependent law categories with tree estimators

Explainable cognitive decline detection in free dialogues with a Machine Learning approach based on pre-trained Large Language Models

Legal Natural Language Processing From 2015 to 2022: A Comprehensive Systematic Mapping Study of Advances and Applications

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