|
Title:
|
NAMED ENTITY RECOGNITION AND INFORMATION
EXTRACTION FOR ARABIC MEDICAL TEXT |
|
Author(s):
|
Jaafar Hammoud, Natalia Dobrenko and Natalia Gusarova |
|
ISBN:
|
978-989-8704-18-4 |
|
Editors:
|
Mário Macedo |
|
Year:
|
2020 |
|
Edition:
|
Single |
|
Keywords:
|
Natural Language Processing, Named Entity Recognition, Arabic, e-Health |
|
Type:
|
Full |
|
First Page:
|
121 |
|
Last Page:
|
127 |
|
Language:
|
English |
|
Cover:
|
|
|
Full Contents:
|
click to dowload 
|
|
Paper Abstract:
|
The article discusses the possibilities of solving NER (Named Entity Recognition) problem for medical texts in Arabic
with limited availability of labeled datasets, as well as computational and specialized linguistic resources. To overcome
them, it is proposed to use recurrent neural networks. In our experiments, we used "BERT-Base, Multilingual Cased"
from Google and Pooled-GRU with Multi-lingual Universal Sentence Encoder (MUSE) from Facebook. Each network
was fine-tuned with our dataset.
The used dataset was obtained from three medical volumes issued by Arabic Encyclopedia. We experimentally evaluated
the effectiveness of tuned models on real NLP (Natural Language Processing) task - medical entities recognition from the
Arabic Medical Encyclopedia and obtained encouraging results. |
|
|
|
|
|
|
