Named Entity Recognition in Portuguese Neurology Text Using CRF

被引：3

作者：

Lopes, Fabio ^{[1
]}

Teixeira, Cesar ^{[1
]}

Oliveira, Hugo Goncalo ^{[1
]}

机构：

[1] Univ Coimbra, Ctr Informat & Syst, Dept Informat Engn, Coimbra, Portugal

来源：

PROGRESS IN ARTIFICIAL INTELLIGENCE, EPIA 2019, PT I | 2019年 / 11804卷

关键词：

Natural Language Processing; Named Entity Recognition; Portuguese clinical text; Machine learning; Conditional Random Fields;

D O I：

10.1007/978-3-030-30241-2_29

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Automatic recognition of named entities from clinical text lightens the work of health professionals by helping in the interpretation and easing tasks such as the population of databases with patient health information. In this study, we evaluated the performance of Conditional Random Fields, a sequence labelling model, for extracting entities from neurology clinical texts written in Portuguese. More than achieving F1-scores of about 73% or 80%, respectively for a relaxed or strict evaluation, the more discriminant features in this task were also analyzed.

引用

页码：336 / 348

页数：13

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