@inbook{e8f56cb3125348fca7bdc074da9930b5,
title = "Employing Natural Language Processing Techniques for the Development of a Voting-Based POS Tagger in the Urdu Language",
abstract = "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 well-known 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.",
author = "Ahmed Raza and Usama Ahmed and Kainat Saleem and Muhammad Sarwar and Momina Shaheen and Farooq, {Muhammad Sohail}",
year = "2025",
month = jan,
day = "17",
doi = "10.4018/979-8-3693-5231-1.ch002",
language = "English",
series = "Advances in Computational Intelligence and Robotics",
publisher = "IGI Global",
pages = "23--46",
booktitle = "Innovations in Optimization and Machine Learning",
}