A Comparison Study of Pre-trained Language Models for Chinese Legal Document Classification

Qin, Ruyu; Huang, Min; Luo, Yutong

doi:10.1109/icaibd55127.2022.9820466

Cited by 5 publications

(2 citation statements)

References 30 publications

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“…Of these, 570,000 were found useable after cleaning (duplicate data, ads, polls, image sharing, @someone, retweets, and other invalid texts were eliminated). Emojis were transformed into corresponding text using 'emojiswitch' library [96]. Word2Vec (a technique for natural language processing) was used to transform the text into numerical models so that a sentiment classification model could process the text data.…”

Section: Methodsmentioning

confidence: 99%

Parks, Green Space, and Happiness: A Spatially Specific Sentiment Analysis Using Microblogs in Shanghai, China

Lai

Deal

2022

Sustainability

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Green spaces, particularly urban parks, provide essential environmental, aesthetic, and recreational benefits to human health, well-being, and happiness. However, traditional forms of investigating people’s perceptions of urban parks, such as questionnaires and interviews, are often time- and resource-intensive and do not always yield results that are transferable across sites. In this study, spatially explicit geolocational information (Sina Weibo check-in data) was utilized to analyze expressions of happiness and well-being in urban parks in Shanghai, China. The results showed significant differences in reported happiness inside and outside urban parks in Shanghai over a 6-month period. Accessibility, naturalness factors, and the frequency of park visits were positively associated with happiness. There existed both commonalities and disparities in the results between residents and non-residents. These findings can provide decision makers and urban planners with a comprehensive and timely overview of urban park use so they can accurately identify park needs and improvements.

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Section: Methodsmentioning

confidence: 99%

Parks, Green Space, and Happiness: A Spatially Specific Sentiment Analysis Using Microblogs in Shanghai, China

Lai

Deal

2022

Sustainability

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“…The classification of legal documents is the basis of legal artificial intelligence tasks and has important research value. Qin et al (2022) found that, compared with machine learning models based on feature engineering and traditional convolutional neural network or recurrent neural network models in the field of NLP, language models pre-trained on an English corpus achieve good performance in classification tasks. Several different pre-trained language models have been studied, and the Chinese legal corpus has been used for pretraining.…”

Section: Fine-tuning Large Language Models For the Legal Domainmentioning

confidence: 99%

Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applications

Yang,

Wang,

Wang

et al. 2024

IJWIS

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Purpose This study aims to adopt a systematic review approach to examine the existing literature on law and LLMs.It involves analyzing and synthesizing relevant research papers, reports and scholarly articles that discuss the use of LLMs in the legal domain. The review encompasses various aspects, including an analysis of LLMs, legal natural language processing (NLP), model tuning techniques, data processing strategies and frameworks for addressing the challenges associated with legal question-and-answer (Q&A) systems. Additionally, the study explores potential applications and services that can benefit from the integration of LLMs in the field of intelligent justice. Design/methodology/approach This paper surveys the state-of-the-art research on law LLMs and their application in the field of intelligent justice. The study aims to identify the challenges associated with developing Q&A systems based on LLMs and explores potential directions for future research and development. The ultimate goal is to contribute to the advancement of intelligent justice by effectively leveraging LLMs. Findings To effectively apply a law LLM, systematic research on LLM, legal NLP and model adjustment technology is required. Originality/value This study contributes to the field of intelligent justice by providing a comprehensive review of the current state of research on law LLMs.

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LAWSUIT: a LArge expert-Written SUmmarization dataset of ITalian constitutional court verdicts

Ragazzi,

Moro,

Guidi

et al. 2024

Artif Intell Law

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Large-scale public datasets are vital for driving the progress of abstractive summarization, especially in law, where documents have highly specialized jargon. However, the available resources are English-centered, limiting research advancements in other languages. This paper introduces LAWSUIT, a collection of 14K Italian legal verdicts with expert-authored abstractive maxims drawn from the Constitutional Court of the Italian Republic. LAWSUIT presents an arduous task with lengthy source texts and evenly distributed salient content. We offer extensive experiments with sequence-to-sequence and segmentation-based approaches, revealing that the latter achieve better results in full and few-shot settings. We openly release LAWSUIT to foster the development and automation of real-world legal applications.

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A Comparison Study of Pre-trained Language Models for Chinese Legal Document Classification

Cited by 5 publications

References 30 publications

Parks, Green Space, and Happiness: A Spatially Specific Sentiment Analysis Using Microblogs in Shanghai, China

Parks, Green Space, and Happiness: A Spatially Specific Sentiment Analysis Using Microblogs in Shanghai, China

Large language models for automated Q&A involving legal documents: a survey on algorithms, frameworks and applications

LAWSUIT: a LArge expert-Written SUmmarization dataset of ITalian constitutional court verdicts

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