UNLT: Urdu Natural Language Toolkit

Result code in IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F22%3AAC6Q79EJ" target="_blank" >RIV/00216208:11320/22:AC6Q79EJ - isvavai.cz</a>

Result on the web

<a href="http://www.cambridge.org/core/journals/natural-language-engineering/article/unlt-urdu-natural-language-toolkit/66306F671F7CB1056A004F1A166E8E30" target="_blank" >http://www.cambridge.org/core/journals/natural-language-engineering/article/unlt-urdu-natural-language-toolkit/66306F671F7CB1056A004F1A166E8E30</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1017/S1351324921000425" target="_blank" >10.1017/S1351324921000425</a>

Result language

angličtina

Original language name

UNLT: Urdu Natural Language Toolkit

Original language description

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.

Czech name

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Czech description

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Type

J_imp - Article in a specialist periodical, which is included in the Web of Science database

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)

Project

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Continuities

—

Publication year

2022

Confidentiality

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

Name of the periodical

Natural Language Engineering

ISSN

1351-3249

e-ISSN

1469-8110

Volume of the periodical

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Issue of the periodical within the volume

2022-1-19

Country of publishing house

GB - UNITED KINGDOM

Number of pages

36

Pages from-to

1-36

UT code for WoS article

000744337800001

EID of the result in the Scopus database

2-s2.0-85124021821