Employing Natural Language Processing Techniques for the Development of a Voting- Based POS Tagger in the Urdu Language

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F26%3AWN5VHV5U" target="_blank" >RIV/00216208:11320/26:WN5VHV5U - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.4018/979-8-3693-5231-1.ch002" target="_blank" >http://dx.doi.org/10.4018/979-8-3693-5231-1.ch002</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.4018/979-8-3693-5231-1.ch002" target="_blank" >10.4018/979-8-3693-5231-1.ch002</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Employing Natural Language Processing Techniques for the Development of a Voting- Based POS Tagger in the Urdu Language

Popis výsledku v původním jazyce

The process of sequence labeling (POS) by assigning syntactic tags to words in the given context is an important role in various NLP applications. The core motive of this work is to tackle the morpho- syntactic category of words in Urdu language. This language has lots of computational challenges because of its dual nature. The work comprises different tasks as initially the authors tracked the best combination of feature sets in terms of CRF to entitle the previous results on two stable and wellknown datasets Bushra Jawaid dataset and CLE dataset. Due to syntactic ambiguity, a state- of- the- art voting method has been introduced which is being implemented to overcome the contradictory results of the different machine learning classifiers. The results show significant improvement in the baseline results as the F1- score on a primary dataset is 94.8% and 95.7% on the succeeding dataset. Long short- term memory (LSTM) is used for one of the most diverse and inflectional tasks like part of speech tagging for the Urdu language by achieving an F1- score of 86.7% and 96.1% respectively for both datasets. © 2025 by IGI Global Scientific Publishing.

Název v anglickém jazyce

Employing Natural Language Processing Techniques for the Development of a Voting- Based POS Tagger in the Urdu Language

Popis výsledku anglicky

The process of sequence labeling (POS) by assigning syntactic tags to words in the given context is an important role in various NLP applications. The core motive of this work is to tackle the morpho- syntactic category of words in Urdu language. This language has lots of computational challenges because of its dual nature. The work comprises different tasks as initially the authors tracked the best combination of feature sets in terms of CRF to entitle the previous results on two stable and wellknown datasets Bushra Jawaid dataset and CLE dataset. Due to syntactic ambiguity, a state- of- the- art voting method has been introduced which is being implemented to overcome the contradictory results of the different machine learning classifiers. The results show significant improvement in the baseline results as the F1- score on a primary dataset is 94.8% and 95.7% on the succeeding dataset. Long short- term memory (LSTM) is used for one of the most diverse and inflectional tasks like part of speech tagging for the Urdu language by achieving an F1- score of 86.7% and 96.1% respectively for both datasets. © 2025 by IGI Global Scientific Publishing.

Druh

C - Kapitola v odborné knize

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

2025

Kód důvěrnosti údajů

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

Název knihy nebo sborníku

Innovations in Optimization and Machine Learning

ISBN

979-8-3693-5233-5

Počet stran výsledku

23

Strana od-do

23-45

Počet stran knihy

504

Název nakladatele

IGI Global

Místo vydání

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Kód UT WoS kapitoly

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