Deep Learning based Part-of-Speech tagging for Assamese using RNN and GRU

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F25%3AK3ZIP3HN" target="_blank" >RIV/00216208:11320/25:K3ZIP3HN - isvavai.cz</a>

Výsledek na webu

<a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196430911&doi=10.1016%2fj.procs.2024.04.161&partnerID=40&md5=937479ee6459c11dad62004e248e8c64" target="_blank" >https://www.scopus.com/inward/record.uri?eid=2-s2.0-85196430911&doi=10.1016%2fj.procs.2024.04.161&partnerID=40&md5=937479ee6459c11dad62004e248e8c64</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2024.04.161" target="_blank" >10.1016/j.procs.2024.04.161</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Deep Learning based Part-of-Speech tagging for Assamese using RNN and GRU

Popis výsledku v původním jazyce

Deep Learning (DL) techniques have been widely used in different Natural Language Processing (NLP) tasks. Parts of Speech (PoS) tagging is one where a wide variety of DL techniques have been experimented with across the languages. Here in the present work, Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) based Parts of Speech taggers have been trained and modelled for Assamese, an Indo Aryan family language. Universal Parts of Speech (UPoS) tag set of 17 tags were used for the experiment. A dataset of 30000 sequences has been used for the work, which is originally a BIS tag set tagged dataset, and customized through conversion from BIS tagged sequences to UPoS tagged sequences. RNN and GRU based systems have been configured using tensorflow platform and the performance measurement was done through accuracy, precision, recall and F1 scores. The accuracy of the RNN based system has been found to be 93.78%. Precision of 94.75 and recall of 93.28 were recorded for the RNN model. Accuracy of 94.38%, precision of 95.44 and recall of 93.7 were recorded for the GRU model. RNN and GRU models respectively yield F1 scores of 94.01 and 94.56. Although PoS tagging with other tag sets like BIS have been attempted by other researchers, UPoS tagging using DL approaches for Assamese is attempted for the first time. And this baseline work with observed accuracies of 93.78 and 94.38 for RNN and GRU respectively, shall serve as reference models for further works. © 2024 Elsevier B.V.. All rights reserved.

Název v anglickém jazyce

Deep Learning based Part-of-Speech tagging for Assamese using RNN and GRU

Popis výsledku anglicky

Deep Learning (DL) techniques have been widely used in different Natural Language Processing (NLP) tasks. Parts of Speech (PoS) tagging is one where a wide variety of DL techniques have been experimented with across the languages. Here in the present work, Recurrent Neural Network (RNN) and Gated Recurrent Unit (GRU) based Parts of Speech taggers have been trained and modelled for Assamese, an Indo Aryan family language. Universal Parts of Speech (UPoS) tag set of 17 tags were used for the experiment. A dataset of 30000 sequences has been used for the work, which is originally a BIS tag set tagged dataset, and customized through conversion from BIS tagged sequences to UPoS tagged sequences. RNN and GRU based systems have been configured using tensorflow platform and the performance measurement was done through accuracy, precision, recall and F1 scores. The accuracy of the RNN based system has been found to be 93.78%. Precision of 94.75 and recall of 93.28 were recorded for the RNN model. Accuracy of 94.38%, precision of 95.44 and recall of 93.7 were recorded for the GRU model. RNN and GRU models respectively yield F1 scores of 94.01 and 94.56. Although PoS tagging with other tag sets like BIS have been attempted by other researchers, UPoS tagging using DL approaches for Assamese is attempted for the first time. And this baseline work with observed accuracies of 93.78 and 94.38 for RNN and GRU respectively, shall serve as reference models for further works. © 2024 Elsevier B.V.. All rights reserved.

Druh

D - Stať ve sborníku

CEP obor

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OECD FORD obor

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

Projekt

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Návaznosti

—

Rok uplatnění

2024

Kód důvěrnosti údajů

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

Název statě ve sborníku

Procedia Comput. Sci.

ISBN

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ISSN

1877-0509

e-ISSN

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Počet stran výsledku

6

Strana od-do

1707-1712

Název nakladatele

Elsevier B.V.

Místo vydání

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Místo konání akce

Dehradun

Datum konání akce

1. 1. 2025

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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