Hybrid Deep Learning Approaches for Assamese Part-of-Speech Tagging Using BIS Tag Set

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AQR3BPYXU" target="_blank" >RIV/00216208:11320/26:QR3BPYXU - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.procs.2025.04.509" target="_blank" >http://dx.doi.org/10.1016/j.procs.2025.04.509</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.procs.2025.04.509" target="_blank" >10.1016/j.procs.2025.04.509</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Hybrid Deep Learning Approaches for Assamese Part-of-Speech Tagging Using BIS Tag Set

Popis výsledku v původním jazyce

In this study, a parts-of-speech (POS) tagger designed exclusively for the Assamese language, utilizing advanced neural network architectures such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) was proposed. The primary objective is to accomplish high-accuracy POS tagging for Assamese text using the Bureau of Indian Standards (BIS) tagset. A large Assamese corpus was created and annotated with BIS-compliant POS tags. After painstaking data preprocessing to clean and tokenize the text, words and tags were turned into numerical representations. The neural network architecture includes an embedding layer with pre-trained word embeddings, an output layer for POS tag prediction, and several LSTM and Bi-LSTM layers that recognize sequential patterns. The models include LSTM at the word and character level and LSTM-CRF and Bi-LSTM-CRF variants. The LSTM-word level model achieved 87.39% accuracy, LSTM-CRF word level 89.13%, Bi-LSTM word level 91.83%, Bi-LSTM-CRF word level 93.23%, Bi-LSTM character level 96.17%, and Bi-LSTM-CRF word level 94.24%. This work outperforms prior Assamese POS tagging efforts and provides useful insights for related languages. © 2024 The Authors. Published by ELSEVIER B.V.

Název v anglickém jazyce

Hybrid Deep Learning Approaches for Assamese Part-of-Speech Tagging Using BIS Tag Set

Popis výsledku anglicky

In this study, a parts-of-speech (POS) tagger designed exclusively for the Assamese language, utilizing advanced neural network architectures such as Long Short-Term Memory (LSTM) and Bidirectional LSTM (Bi-LSTM) was proposed. The primary objective is to accomplish high-accuracy POS tagging for Assamese text using the Bureau of Indian Standards (BIS) tagset. A large Assamese corpus was created and annotated with BIS-compliant POS tags. After painstaking data preprocessing to clean and tokenize the text, words and tags were turned into numerical representations. The neural network architecture includes an embedding layer with pre-trained word embeddings, an output layer for POS tag prediction, and several LSTM and Bi-LSTM layers that recognize sequential patterns. The models include LSTM at the word and character level and LSTM-CRF and Bi-LSTM-CRF variants. The LSTM-word level model achieved 87.39% accuracy, LSTM-CRF word level 89.13%, Bi-LSTM word level 91.83%, Bi-LSTM-CRF word level 93.23%, Bi-LSTM character level 96.17%, and Bi-LSTM-CRF word level 94.24%. This work outperforms prior Assamese POS tagging efforts and provides useful insights for related languages. © 2024 The Authors. Published by ELSEVIER B.V.

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

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Rok uplatnění

2025

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

18770509

e-ISSN

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Počet stran výsledku

10

Strana od-do

2469-2478

Název nakladatele

Elsevier B.V.

Místo vydání

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Místo konání akce

Dehradun

Datum konání akce

1. 1. 2026

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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