The linked legal data landscape: linking legal data across different countries

Filtz, Erwin; Kirrane, Sabrina; Polleres, Axel

doi:10.1007/s10506-021-09282-8

Cited by 21 publications

(9 citation statements)

References 40 publications

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“…For example, Bhattacharya et al [101] compared classical and PLM-based approaches on legal summarization with a focus on judgments from the Indian Supreme Court. In order to build an Austrian legal knowledge graph, Filtz et al [102] evaluated classical techniques (e.g., CRF) and different PLMs on a legal entity extraction task. Mandal et al [103] presented another related effort in catchphrase extraction and explored different deep learning methods (e.g., CNN and GRU).…”

Section: Legal Nlp Benchmarksmentioning

confidence: 99%

On the Effectiveness of Pre-Trained Language Models for Legal Natural Language Processing: An Empirical Study

Song

Gao

et al. 2022

IEEE Access

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We present the first comprehensive empirical evaluation of pre-trained language models (PLMs) for legal natural language processing (NLP) in order to examine their effectiveness in this domain.Our study covers eight representative and challenging legal datasets, ranging from 900 to 57K samples, across five NLP tasks: binary classification, multi-label classification, multiple choice question answering, summarization and information retrieval. We first run unsupervised, classical machine learning and/or non-PLM based deep learning methods on these datasets, and show that baseline systems' performance can be 4%∼35% lower than that of PLM-based methods. Next, we compare general-domain PLMs and those specifically pre-trained for the legal domain, and find that domain-specific PLMs demonstrate 1%∼5% higher performance than general-domain models, but only when the datasets are extremely close to the pretraining corpora. Finally, we evaluate six general-domain state-of-the-art systems, and show that they have limited generalizability to legal data, with performance gains from 0.1% to 1.2% over other PLM-based methods. Our experiments suggest that both general-domain and domain-specific PLM-based methods generally achieve better results than simpler methods on most tasks, with the exception of the retrieval task, where the best-performing baseline outperformed all PLM-based methods by at least 5%. Our findings can help legal NLP practitioners choose the appropriate methods for different tasks, and also shed light on potential future directions for legal NLP research.

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Section: Legal Nlp Benchmarksmentioning

confidence: 99%

On the Effectiveness of Pre-Trained Language Models for Legal Natural Language Processing: An Empirical Study

Song

Gao

et al. 2022

IEEE Access

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“…It is an important embodiment of the intellectualization of the legal assistant model of economic and trade management. On the one hand, this algorithm model can provide a reasonable reference for the judges of economic and trade cases and improve the efficiency of handling cases; on the other hand, this model can horizontally compare similar economic and trade cases, provide judgment reference for different judges, and avoid the artificial deviation of different judgment results in the same case [11][12][13]. e prediction and judgment technology of the economic and trade management law refers to a new technology in which the computer predicts the judgment result according to the detailed case description.…”

Section: Related Workmentioning

confidence: 99%

Optimization of the Economic and Trade Management Legal Model Based on the Support Vector Machine Algorithm and Logistic Regression Algorithm

Nie

Bai

Nie

et al. 2022

Mathematical Problems in Engineering

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Nowadays, various algorithms are widely used in the field of economy and trade, and economic and trade management laws also need to introduce scientific and effective data models for optimization. In this paper, support vector machine algorithm and logistic regression algorithm are used to analyze and process the actual economic and trade case data and bank loan user data, and a hybrid model of support vector machine and logistic regression is established. This study first introduces the basic definitions and contents of the support vector machine algorithm and logistic regression algorithm, and then constructs a hybrid model by randomly dividing the data, first using the support vector machine algorithm to calculate the results, and then inputting them into the logistic regression algorithm. The first mock exam is that the efficiency of the hybrid model is much higher than that of the single model. This study mainly optimizes and upgrades the legal system of economic and trade management from two aspects. In the prediction of economic and trade legal cases, the hybrid model is significantly better than FastText and LSTM models in accuracy and macro recall performance. In terms of credit risk prediction of economic and trade loan users, the subset most likely to default in the loan customer set is obtained.

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“…In addition, the learning curve for formulating questions in graph-oriented languages has led to the search for alternative approaches. In recent times, several works have also emerged that use for the first time the potential of knowledge graphs to overcome some of the gaps of neural solutions, such as the lack of interpretability and the need for large amounts of data for training (Tong et al, 2019;Sovrano et al, 2020;Huang et al, 2020a;Filtz et al, 2021). In addition, technologies such as GPT-3 7 have been developed to convert questions formulated in natural language to their equivalent in graph-oriented language.…”

Section: Towards a New Family Of Solutionsmentioning

confidence: 99%

“…Although the process on how the extraction function is usually learned is well described in (Zou et al, 2014). The good news is that the use of Knowledge Graphs of a legal nature is being investigated to obtain better results (Filtz, 2017;Huang et al, 2020a;Filtz et al, 2021).…”

Section: Structured Sourcesmentioning

confidence: 99%

A Survey on Legal Question Answering Systems

Martinez‐Gil¹

2021

Preprint

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Many legal professionals think that the explosion of information about local, regional, national, and international legislation makes their practice more costly, time-consuming, and even error-prone. The two main reasons for this are that most legislation is usually unstructured, and the tremendous amount and pace with which laws are released causes information overload in their daily tasks. In the case of the legal domain, the research community agrees that a system allowing to generate automatic responses to legal questions could substantially impact many practical implications in daily activities.The degree of usefulness is such that even a semi-automatic solution could significantly help to reduce the workload to be faced. This is mainly because a Question Answering system could be able to automatically process a massive amount of legal resources to answer a question or doubt in seconds, which means that it could save resources in the form of effort, money, and time to many professionals in the legal sector. In this work, we quantitatively and qualitatively survey the solutions that currently exist to meet this challenge.

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The linked legal data landscape: linking legal data across different countries

Cited by 21 publications

References 40 publications

On the Effectiveness of Pre-Trained Language Models for Legal Natural Language Processing: An Empirical Study

On the Effectiveness of Pre-Trained Language Models for Legal Natural Language Processing: An Empirical Study

Optimization of the Economic and Trade Management Legal Model Based on the Support Vector Machine Algorithm and Logistic Regression Algorithm

A Survey on Legal Question Answering Systems

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