Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)最新文献_第6页

A comparative analysis of text embedding approach to extract named entities in Portuguese legal documents 葡萄牙法律文件中命名实体提取的文本嵌入方法比较分析

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18255

Hyan H. N. Batista, André C. A. Nascimento, Rafael Ferreira Melo, Pericles Miranda, Isabel W. S. Maldonado, José L. M. Coelho Filho

{"title":"A comparative analysis of text embedding approach to extract named entities in Portuguese legal documents","authors":"Hyan H. N. Batista, André C. A. Nascimento, Rafael Ferreira Melo, Pericles Miranda, Isabel W. S. Maldonado, José L. M. Coelho Filho","doi":"10.5753/eniac.2021.18255","DOIUrl":"https://doi.org/10.5753/eniac.2021.18255","url":null,"abstract":"A petição inicial é um dos componentes mais importantes de um processo civil, de modo que a automatização da análise desses documentos pode diminuir o tempo necessário para que se cumpra a fase postulatória. O corpo de qualificação das partes, por sua vez, é a seção nesse documento onde são expostas as informações a respeito das entidades envolvidas no processo. Este artigo propõe o uso de técnicas de extração de entidades nomeadas no problema de identificação e extração de informações em petições iniciais. Para tal, foi produzida uma base de dados composta por corpos de qualificação das partes de petições iniciais extraídas de processos advindos de tribunais brasileiros. Foram treinados, avaliados e comparados sete modelos BiLSTM-CRF com combinações distintas de representações vetoriais de palavras, a fim de se investigar seus efeitos na performance de um algoritmo com essa arquitetura e, dessa forma, aprimorar o reconhecimento de entidades jurídicas em textos legais. Ao contrário de outros trabalhos baseados em BiLSTM-CRF para tarefas de NER no domínio jurídico, esta pesquisa dá ênfase não às arquiteturas empregadas, mas sim aos métodos de representação de texto usados. Os experimentos executados com o corpus desenvolvido mostram que o empilhamento de incorporações de caracteres, palavras e pooled FLAIR embeddings é a combinação preferível para extrair-se o melhor desempenho possível de modelos híbridos BiLSTM-CRF.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"83 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132742160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Assessing Fair Machine Learning Strategies Through a Fairness-Utility Trade-off Metric 通过公平-效用权衡度量评估公平机器学习策略

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18288

Luiz Fernando F. P. de Lima, D. R. D. Ricarte, C. Siebra

引用次数: 1

Time Series Classification using Shape Features based on Angle Statistics 基于角度统计的形状特征时间序列分类

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18276

Bionda Rozin, D. C. G. Pedronette

引用次数: 0

Exploring model transfer strategies for sentiment analysis in Twitter 探索Twitter情感分析的模型迁移策略

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18236

Eliseu Guimarães, Jonnathan Carvalho, A. Paes, Alexandre Plastino

引用次数: 0

A clustering algorithm to evaluate the attitude of Brazilian researchers regarding open access research data 一个聚类算法来评估巴西研究人员对开放获取研究数据的态度

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18290

Bruna S. Freitas, Diego Bottero, Giancarlo Lucca, E. N. Borges, H. Santos, G. Dimuro

引用次数: 0

MISLA²: A System to Information Retrieval in Labour Lawsuits using Legal Ontologies and Regular Expressions 基于法律本体和正则表达式的劳动诉讼信息检索系统

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18267

C. Rodrigues, Bruno J. T. Fernandes, L. Silva, David J. Barrientos, Allana L. S. Rocha, Paulo Christiano Sobral, Bruno Souza, Dionizio Feitosa, Mabel Guimarães, Juliana Barreto

{"title":"MISLA²: A System to Information Retrieval in Labour Lawsuits using Legal Ontologies and Regular Expressions","authors":"C. Rodrigues, Bruno J. T. Fernandes, L. Silva, David J. Barrientos, Allana L. S. Rocha, Paulo Christiano Sobral, Bruno Souza, Dionizio Feitosa, Mabel Guimarães, Juliana Barreto","doi":"10.5753/eniac.2021.18267","DOIUrl":"https://doi.org/10.5753/eniac.2021.18267","url":null,"abstract":"Electronic Legal Proceedings are a worldwide legal phenomena, allowing the use of computerized systems for the creation and monitoring of procedural acts in the most diverse legal bodies. On one hand, it allows greater transparency in the conduct of procedural acts, on the other, it has contributed to the bottleneck of open but unresolved lawsuits each year. Nowadays, Information Retrieval to automate the processing of these procedural objects is at the forefront of computer systems for Law. In this study, we present MISLA2, a system to retrieve orders and preliminaries from judicial labour sentences through ontological models built from previous cases. Instead of tied and difficult-to-maintain domain specification models, we demonstrate how light ontologies, in conjunction with regular expressions for extracting significant portions of the text, can achieve the desired results. In addition, empirical experiments carried out with real labour lawsuits evidence that results are quite promising.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128271989","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

A Comparative Analysis of Machine Learning Named Entity Recognition Tools for the Brazilian and European Portuguese Language Variants 机器学习命名实体识别工具对巴西和欧洲葡萄牙语变体的比较分析

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18257

Breno David Lopes Pinheiro, E. Souza, Douglas Vitório, H. O. Albuquerque

引用次数: 0

Detection of weapon possession and fire in Public Safety surveillance cameras 在公共安全监控摄像机中发现武器持有和火灾

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18261

Natan Moura, J. Gondim, Daniela Barreiro Claro, Marlo Souza, Roberto de Cerqueira Figueiredo

引用次数: 0

Hybrid Swarm Enhanced Classifier Ensembles 混合群增强分类器集成

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18263

José Matheus Lacerda Barbosa, Adriano Marabuco de Albuquerque Lima, P. M. Mattos Neto, Adriana Lima de Oliveira

引用次数: 0

Applying machine learning to assist the diagnosis of COVID-19 from blood and urine exams 应用机器学习帮助从血液和尿液检查中诊断COVID-19

Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021) Pub Date : 2021-11-29 DOI: 10.5753/eniac.2021.18258

Jessica Carolina Matos D'Almeida Santos, Lilian Berton

{"title":"Applying machine learning to assist the diagnosis of COVID-19 from blood and urine exams","authors":"Jessica Carolina Matos D'Almeida Santos, Lilian Berton","doi":"10.5753/eniac.2021.18258","DOIUrl":"https://doi.org/10.5753/eniac.2021.18258","url":null,"abstract":"The COVID-19 pandemic declared in March 2020 by the World Health Organization (WHO) challenged the health system of several countries with the growing number of infected people. During the pandemic's peak in Europe, the low incidence of infection in South Korea drew the international community's attention, since not long ago that country was considered the epicenter of the pandemic outside its origin, in China. The mass testing protocol and tracing policies were pointed out as the formula for South Korean success, however, in view of the high demand and little supply of diagnostic tests for COVID-19 in the market, this strategy proved to be unfeasible to be implemented mainly in countries with large populations and with few financial resources, such as Brazil. There is also the aggravating factor regarding the effectiveness of the tests currently available, especially the rapid serology test with a high rate of false negatives. In order to offer a screening method for the application of tests, this work aims to develop a predictive model for assisting the identification of COVID-19 infection in suspected patients based on data from clinical laboratory examinations, such as blood count and urine tests. The data used comes from three sources in Sao Paulo and are hosted in the COVID-19 Data Sharing/BR Repository, a shared database of Sao Paulo Research Foundation (FAPESP). The work also proposes a comparison between balanced × imbalanced dataset and traditional × ensemble algorithms for this problem.","PeriodicalId":318676,"journal":{"name":"Anais do XVIII Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2021)","volume":"102 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-11-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128579147","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 1