Developing Indonesian Medical Corpora Using the Latent Dirichlet Allocation Method and Filtering Out Non-medical Terms and Non-noun Words
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F23%3AS5VX3HW2" target="_blank" >RIV/00216208:11320/23:S5VX3HW2 - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173433304&doi=10.1109%2fCOSITE60233.2023.10250069&partnerID=40&md5=78adff8fa254f59f37a23b829cc04c65" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85173433304&doi=10.1109%2fCOSITE60233.2023.10250069&partnerID=40&md5=78adff8fa254f59f37a23b829cc04c65</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1109/cosite60233.2023.10250069" target="_blank" >10.1109/cosite60233.2023.10250069</a>
Alternative languages
Result language
angličtina
Original language name
Developing Indonesian Medical Corpora Using the Latent Dirichlet Allocation Method and Filtering Out Non-medical Terms and Non-noun Words
Original language description
"As part of the research collaboration among BRIN, Solusi247, and the Harapan Kita Heart and Blood Vessel Hospital to develop an Indonesian medical speech recognition system that requires a medical text corpus, we have collected 300,063 medical Q&A articles (HTML files) about various diseases. This study is a preliminary step before developing the medical speech recognition system. As is known, a symptom of a disease can also be a symptom of another disease. For example, tiredness can be a flu or low blood pressure symptom. We intended to create medical corpora by clustering the medical documents according to the type of disease being conversed. The clustering was done using the Latent Dirichlet Allocation (LDA) method and the c_v measure. Before the clustering, we modified the articles by normalizing the medical terms and treating certain compound words as a single word. In the experiments, we used stop words containing non-medical terms and other stop words consisting of nonnouns. The experimental results showed that, on average, the c_v coherence score of the modified corpus is better than the original corpus, with a mean difference of 0.0122 and 0.0197, showing that the modification has a good impact. © 2023 IEEE."
Czech name
—
Czech description
—
Classification
Type
D - Article in proceedings
CEP classification
—
OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
—
Continuities
—
Others
Publication year
2023
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
"Proceeding - Int. Conf. Comput. Syst., Inf. Technol., Electr. Eng.: Sustain. Dev. Smart Innov. Syst., COSITE"
ISBN
979-835034306-9
ISSN
—
e-ISSN
—
Number of pages
6
Pages from-to
43-48
Publisher name
Institute of Electrical and Electronics Engineers Inc.
Place of publication
—
Event location
Cham
Event date
Jan 1, 2023
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
—