Incremental Learning for GRU and RNN-based Assamese UPoS Tagger

Kód výsledku v 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>

Výsledek na webu

<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>

Jazyk výsledku

angličtina

Název v původním jazyce

Incremental Learning for GRU and RNN-based Assamese UPoS Tagger

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Incremental Learning for GRU and RNN-based Assamese UPoS Tagger

Popis výsledku anglicky

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.

Druh

J_SC - Článek v periodiku v databázi SCOPUS

CEP obor

—

OECD FORD obor

10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)

Projekt

—

Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

International Journal of Advanced Computer Science and Applications

ISSN

2158-107X

e-ISSN

—

Svazek periodika

15

Číslo periodika v rámci svazku

6

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

7

Strana od-do

305-311

Kód UT WoS článku

—

EID výsledku v databázi Scopus

2-s2.0-85197553119