Employing Natural Language Processing Techniques for the Development of a Voting- Based POS Tagger in the Urdu Language
The result's identifiers
Result code in 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>
Result on the web
<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>
Alternative languages
Result language
angličtina
Original language name
Employing Natural Language Processing Techniques for the Development of a Voting- Based POS Tagger in the Urdu Language
Original language description
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.
Czech name
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Czech description
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Classification
Type
C - Chapter in a specialist book
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
2025
Confidentiality
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů
Data specific for result type
Book/collection name
Innovations in Optimization and Machine Learning
ISBN
979-8-3693-5233-5
Number of pages of the result
23
Pages from-to
23-45
Number of pages of the book
504
Publisher name
IGI Global
Place of publication
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UT code for WoS chapter
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