Incremental Learning for GRU and RNN-based Assamese UPoS Tagger
The result's identifiers
Result code in IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AU4IAHNHY" target="_blank" >RIV/00216208:11320/25:U4IAHNHY - isvavai.cz</a>
Result on the web
<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197553119&doi=10.14569%2fIJACSA.2024.0150633&partnerID=40&md5=430e0997b35e34907226d6b10395d512" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85197553119&doi=10.14569%2fIJACSA.2024.0150633&partnerID=40&md5=430e0997b35e34907226d6b10395d512</a>
DOI - Digital Object Identifier
<a href="http://dx.doi.org/10.14569/IJACSA.2024.0150633" target="_blank" >10.14569/IJACSA.2024.0150633</a>
Alternative languages
Result language
angličtina
Original language name
Incremental Learning for GRU and RNN-based Assamese UPoS Tagger
Original language description
This research paper introduces a novel approach to enhance the performance of Universal Part-of-Speech (UPoS) tagging for the low-resource language Assamese, employing Recurrent Neural Networks (RNNs) and Gated Recurrent Units (GRUs). The novelty added in this study is the experimentation with Incremental Learning, a dynamic paradigm allowing the models to continually refine their understanding as they encounter new set of linguistic data. The proposed model utilizes the strengths of GRUs and traditional RNNs to capture long range sequential dependencies and contextual information within Assamese sentences. Incorporation of Incremental Learning ensures the model’s adaptability to evolving linguistic patterns, particularly crucial for under-resourced languages like Assamese. Experimental results showcase the superiority of the proposed approach, achieving state-of-the-art accuracy in Assamese UPoS tagging. The research not only contributes to the field of natural language processing but also addresses the specific challenges posed by under-resourced languages. The significance of Incremental Learning is highlighted, showcasing its role in dynamically updating the model’s knowledge base with new UPoS-tagged data. This feature proves essential in real-world scenarios where language evolves, ensuring sustained optimal performance in Assamese UPoS tagging. The paper presents the details of the innovative framework for UPoS tagging in Assamese, combining the significance of Incremental Learning with Deep Learning techniques, pushing the boundaries of natural language processing models for low resource languages exploring the importance of dynamic learning paradigms. © (2024) Science and Information Organization. All rights reserved.
Czech name
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Czech description
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Classification
Type
J<sub>SC</sub> - Article in a specialist periodical, which is included in the SCOPUS database
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
2024
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Name of the periodical
International Journal of Advanced Computer Science and Applications
ISSN
2158-107X
e-ISSN
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Volume of the periodical
15
Issue of the periodical within the volume
6
Country of publishing house
US - UNITED STATES
Number of pages
7
Pages from-to
305-311
UT code for WoS article
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EID of the result in the Scopus database
2-s2.0-85197553119