UNLT: Urdu Natural Language Toolkit

This study describes a Natural Language Processing (NLP) toolkit, as the first contribution of a larger project, for an under-resourced language—Urdu. In previous studies, standard NLP toolkits have been developed for English and many other languages. There is also a dire need for standard text proc...

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Bibliographic Details
Published in:Natural language engineering 2023-07, Vol.29 (4), p.942-977
Main Authors: Shafi, Jawad, Iqbal, Hafiz Rizwan, Nawab, Rao Muhammad Adeel, Rayson, Paul
Format: Article
Language:English
Subjects:
Algorithms
Compound words
Datasets
Dictionaries
Language
Language modeling
Linguistics
Machine learning
Markov analysis
Markov chains
Maximum entropy
Morphemes
N-Gram language models
Natural language
Natural language processing
Sentence structure
Smoothing
Tagging (Computational linguistics)
Toolkits
Training
Urdu language
Word processing
Words (language)
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Summary:This study describes a Natural Language Processing (NLP) toolkit, as the first contribution of a larger project, for an under-resourced language—Urdu. In previous studies, standard NLP toolkits have been developed for English and many other languages. There is also a dire need for standard text processing tools and methods for Urdu, despite it being widely spoken in different parts of the world with a large amount of digital text being readily available. This study presents the first version of the UNLT (Urdu Natural Language Toolkit) which contains three key text processing tools required for an Urdu NLP pipeline; word tokenizer, sentence tokenizer, and part-of-speech (POS) tagger. The UNLT word tokenizer employs a morpheme matching algorithm coupled with a state-of-the-art stochastic n-gram language model with back-off and smoothing characteristics for the space omission problem. The space insertion problem for compound words is tackled using a dictionary look-up technique. The UNLT sentence tokenizer is a combination of various machine learning, rule-based, regular-expressions, and dictionary look-up techniques. Finally, the UNLT POS taggers are based on Hidden Markov Model and Maximum Entropy-based stochastic techniques. In addition, we have developed large gold standard training and testing data sets to improve and evaluate the performance of new techniques for Urdu word tokenization, sentence tokenization, and POS tagging. For comparison purposes, we have compared the proposed approaches with several methods. Our proposed UNLT, the training and testing data sets, and supporting resources are all free and publicly available for academic use.
ISSN:1351-3249
1469-8110
DOI:10.1017/S1351324921000425