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The Brazilian judiciary has a large workload, resulting in a long time to finish legal proceedings. Brazilian National Council of Justice has established in Resolution 469/2022 formal guidance for document and process digitalization opening…

计算与语言 · 计算机科学 2023-06-02 Daniel da Silva Junior , Paulo Roberto dos S. Corval , Aline Paes , Daniel de Oliveira

Semantic Role Labeling (SRL) is a Natural Language Processing task that enables the detection of events described in sentences and the participants of these events. For Brazilian Portuguese (BP), there are two studies recently concluded…

计算与语言 · 计算机科学 2017-04-12 Nathan Siegle Hartmann , Magali Sanches Duran , Sandra Maria Aluísio

Named Entity Recognition (NER) is a fundamental Natural Language Processing (NLP) task that aims to identify and classify entity mentions in texts across different categories. While languages such as English possess a large number of…

Semantic legal metadata provides information that helps with understanding and interpreting legal provisions. Such metadata is therefore important for the systematic analysis of legal requirements. However, manually enhancing a large legal…

软件工程 · 计算机科学 2020-01-31 Amin Sleimi , Nicolas Sannier , Mehrdad Sabetzadeh , Lionel Briand , Marcello Ceci , John Dann

Scene text image contains two levels of contents: visual texture and semantic information. Although the previous scene text recognition methods have made great progress over the past few years, the research on mining semantic information to…

计算机视觉与模式识别 · 计算机科学 2020-03-30 Deli Yu , Xuan Li , Chengquan Zhang , Junyu Han , Jingtuo Liu , Errui Ding

Recent advances in language representation using neural networks have made it viable to transfer the learned internal states of a trained model to downstream natural language processing tasks, such as named entity recognition (NER) and…

计算与语言 · 计算机科学 2020-02-28 Fábio Souza , Rodrigo Nogueira , Roberto Lotufo

A basic task for most Legal Artificial Intelligence (Legal AI) applications is Named Entity Recognition (NER). However, texts produced in the context of legal practice make references to entities that are not trivially recognized by the…

Identifying named entities such as a person, location or organization, in documents can highlight key information to readers. Training Named Entity Recognition (NER) models requires an annotated data set, which can be a time-consuming…

计算与语言 · 计算机科学 2022-12-20 Ting Wai Terence Au , Ingemar J. Cox , Vasileios Lampos

Statutory article retrieval (SAR), the task of retrieving statute law articles relevant to a legal question, is a promising application of legal text processing. In particular, high-quality SAR systems can improve the work efficiency of…

信息检索 · 计算机科学 2023-01-31 Antoine Louis , Gijs van Dijck , Gerasimos Spanakis

This work investigates legal concepts and their expression in Portuguese, concentrating on the "Order of Attorneys of Brazil" Bar exam. Using a corpus formed by a collection of multiple-choice questions, three norms related to the Ethics…

计算与语言 · 计算机科学 2018-10-23 Alessandra Cid , Alexandre Rademaker , Bruno Cuconato , Valeria de Paiva

Effective wastewater and stormwater management is essential for urban sustainability and environmental protection. Extracting structured knowledge from reports and regulations is challenging due to domainspecific terminology and…

计算与语言 · 计算机科学 2025-06-03 Franco Alberto Cardillo , Franca Debole , Francesca Frontini , Mitra Aelami , Nanée Chahinian , Serge Conrad

Legal Entity Recognition (LER) is critical in automating legal workflows such as contract analysis, compliance monitoring, and litigation support. Existing approaches, including rule-based systems and classical machine learning models,…

计算与语言 · 计算机科学 2025-07-18 Duraimurugan Rajamanickam

Named Entity Recognition (NER) is the task of identifying and classifying named entities in unstructured text. In the legal domain, named entities of interest may include the case parties, judges, names of courts, case numbers, references…

计算与语言 · 计算机科学 2020-12-21 Stavroula Skylaki , Ali Oskooei , Omar Bari , Nadja Herger , Zac Kriegman

The Web has become a potentially infinite information resource, turning into an essential tool for many daily activities. This resulted in an increase in the amount of information available in users' contexts that is not taken into account…

信息检索 · 计算机科学 2018-10-11 Carlos M. Lorenzetti

Data derived from the realm of the social sciences is often produced in digital text form, which motivates its use as a source for natural language processing methods. Researchers and practitioners have developed and relied on artificial…

计算与语言 · 计算机科学 2024-06-04 Raysa Benatti , Fabiana Severi , Sandra Avila , Esther Luna Colombini

Retrieving pertinent documents from various data sources with diverse characteristics poses a significant challenge for Document Retrieval Systems. The complexity of this challenge is further compounded when accounting for the semantic…

信息检索 · 计算机科学 2025-08-29 Apurva Kulkarni , Chandrashekar Ramanathan , Vinu E Venugopal

Named Entity Recognition (NER) is a critical component of Natural Language Processing (NLP) for extracting structured information from unstructured text. However, for low-resource languages like Catalan, the performance of NER systems often…

The high volume of research focusing on extracting patient's information from electronic health records (EHR) has led to an increase in the demand for annotated corpora, which are a very valuable resource for both the development and…

This paper introduces JurisTCU, a Brazilian Portuguese dataset for legal information retrieval (LIR). The dataset is freely available and consists of 16,045 jurisprudential documents from the Brazilian Federal Court of Accounts, along with…

The recent application of LLMs to the legal field has spurred the creation of benchmarks across various jurisdictions and languages. However, no benchmark has yet been specifically designed for the Portuguese legal system. In this work, we…

计算与语言 · 计算机科学 2025-02-25 Beatriz Canaverde , Telmo Pessoa Pires , Leonor Melo Ribeiro , André F. T. Martins
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