A Vietnamese Named Entity Recognition System for COVID-19 Articles
2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022
; 2022.
Article
in English
| Scopus | ID: covidwho-2230986
ABSTRACT
This paper presents a named entity recognition system for the specific domain of Vietnamese COVID-19 news articles. By incorporating manually selected and domain-specific features into a simple deep learning architecture, the system can identify a wide range of custom named entities relevant in the context of COVID-19 and future epidemics. Using high-dimensional embedding vectors in combination with part-of-speech tags and additional features, the system achieves an F score of about 90.41%, surpassing or coming close to results by other models that are more complicated or pre-Trained and fine-Tuned. © 2022 IEEE.
Full text:
Available
Collection:
Databases of international organizations
Database:
Scopus
Type of study:
Prognostic study
Language:
English
Journal:
2022 IEEE MIT Undergraduate Research Technology Conference, URTC 2022
Year:
2022
Document Type:
Article
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