Dependency Parsing Using Recurrent Neural Network on Myanmar Language
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AS7AQNJ7K" target="_blank" >RIV/00216208:11320/26:S7AQNJ7K - isvavai.cz</a>
Result on the web
<a href="http://dx.doi.org/10.1007/978-981-96-1531-5_36" target="_blank" >http://dx.doi.org/10.1007/978-981-96-1531-5_36</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.1007/978-981-96-1531-5_36" target="_blank" >10.1007/978-981-96-1531-5_36</a>
Alternative languages
Result language
angličtina
Original language name
Dependency Parsing Using Recurrent Neural Network on Myanmar Language
Original language description
Word segmentation, part of speech of tagging and dependency parsing are the important role in Natural Language Processing (NLP). The POS and dependency parsing information are also necessary for NLP’s applications such as machine translation (MT), information retrieval (IR), etc. Although there are many research efforts in this process, there is still necessary to develop standard model for the Myanmar Language. This system uses Recurrent Neural Network (RNN) to keep away from errors and improve segmentation by utilizing POS data. In this paper, this system compares BILSTM and Hidden Markov Model (HMM) and the performances showed that precision, recall, F1 score, support and confusion matrix by using MLPOS. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.
Czech name
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Czech description
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Classification
Type
D - Article in proceedings
CEP classification
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OECD FORD branch
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Result continuities
Project
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Continuities
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Others
Publication year
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Article name in the collection
Lect. Notes Electr. Eng.
ISBN
978-981-96-1530-8
ISSN
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e-ISSN
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Number of pages
11
Pages from-to
368-378
Publisher name
Springer Science and Business Media Deutschland GmbH
Place of publication
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Event location
Miyazaki
Event date
Jan 1, 2026
Type of event by nationality
WRD - Celosvětová akce
UT code for WoS article
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