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UNLT:Urdu Natural Language Toolkit
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Abstract:
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.
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URL: https://eprints.lancs.ac.uk/id/eprint/165032/1/UNLT_Urdu_Natural_Language_Toolkit_V_15_November_2021.pdf https://doi.org/10.1017/S1351324921000425 https://eprints.lancs.ac.uk/id/eprint/165032/
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Textual Variations Affect Human Judgements of Sentiment Values
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The ParlaMint corpora of parliamentary proceedings
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In: Lang Resour Eval (2022)
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MasakhaNER: Named entity recognition for African languages
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In: EISSN: 2307-387X ; Transactions of the Association for Computational Linguistics ; https://hal.inria.fr/hal-03350962 ; Transactions of the Association for Computational Linguistics, The MIT Press, 2021, ⟨10.1162/tacl⟩ (2021)
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Multilingual comparable corpora of parliamentary debates ParlaMint 2.1
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Linguistically annotated multilingual comparable corpora of parliamentary debates ParlaMint.ana 2.1
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Understanding who uses Reddit: Profiling individuals with a self-reported bipolar disorder diagnosis ...
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How have Natural Language Processing methods been applied to the study of bipolar? Protocol for scoping review ...
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How have Natural Language Processing methods been applied to the study of bipolar? Protocol for scoping review ...
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How have Natural Language Processing methods been applied to the study of bipolar? Protocol for scoping review ...
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How have Natural Language Processing methods been applied to the study of bipolar? Protocol for scoping review ...
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How have Natural Language Processing methods been applied to the study of bipolar? Protocol for scoping review ...
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How have Natural Language Processing methods been applied to the study of bipolar? Protocol for scoping review ...
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How have Natural Language Processing methods been applied to the study of bipolar? Protocol for scoping review ...
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How have Natural Language Processing methods been applied to the study of bipolar? Protocol for scoping review ...
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Extending the key semantic domains method beyond English corpora:Wmatrix version 5
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