Part of Speech Tagging in Urdu: Comparison of Machine and Deep Learning Approaches
Identifikátory výsledku
Kód výsledku v IS VaVaI
<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216208%3A11320%2F19%3A10427036" target="_blank" >RIV/00216208:11320/19:10427036 - isvavai.cz</a>
Výsledek na webu
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DOI - Digital Object Identifier
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Alternativní jazyky
Jazyk výsledku
angličtina
Název v původním jazyce
Part of Speech Tagging in Urdu: Comparison of Machine and Deep Learning Approaches
Popis výsledku v původním jazyce
In Urdu, part of speech (POS) tagging is a challenging task as it is both inflectionally and derivationally rich morphological language. Verbs are generally conceived a highly inflected object in Urdu comparatively to nouns. POS tagging is used as a preliminary linguistic text analysis in diverse natural language processing domains such as speech processing, information extraction, machine translation, and others. It is a task that first identifies appropriate syntactic categories for each word in running text and second assigns the predicted syntactic tag to all concerned words. The current work is the extension of our previous work. Previously, we presented conditional random field (CRF)-based POS tagger with both language dependent and independent feature set. However, in the current study, we offer: 1) the implementation of both machine and deep learning models for Urdu POS tagging task with well-balanced language-independent feature set and 2) to highlight diverse challenges which cause Urdu POS task a challenging one. In this research, we demonstrated the effectiveness of machine learning and deep learning models for Urdu POS task. Empirically, we have evaluated the performance of all models on two benchmark datasets. The core models evaluated in this study are CRF, support vector machine (SVM), two variants of the deep recurrent neural network (DRNN), and a variant of n-gram Markov model the bigram hidden Markov model (HMM). The two variants of DRRN models evaluated include forward long short-term memory (LSTM)-RNN and LSTM-RNN with CRF output.
Název v anglickém jazyce
Part of Speech Tagging in Urdu: Comparison of Machine and Deep Learning Approaches
Popis výsledku anglicky
In Urdu, part of speech (POS) tagging is a challenging task as it is both inflectionally and derivationally rich morphological language. Verbs are generally conceived a highly inflected object in Urdu comparatively to nouns. POS tagging is used as a preliminary linguistic text analysis in diverse natural language processing domains such as speech processing, information extraction, machine translation, and others. It is a task that first identifies appropriate syntactic categories for each word in running text and second assigns the predicted syntactic tag to all concerned words. The current work is the extension of our previous work. Previously, we presented conditional random field (CRF)-based POS tagger with both language dependent and independent feature set. However, in the current study, we offer: 1) the implementation of both machine and deep learning models for Urdu POS tagging task with well-balanced language-independent feature set and 2) to highlight diverse challenges which cause Urdu POS task a challenging one. In this research, we demonstrated the effectiveness of machine learning and deep learning models for Urdu POS task. Empirically, we have evaluated the performance of all models on two benchmark datasets. The core models evaluated in this study are CRF, support vector machine (SVM), two variants of the deep recurrent neural network (DRNN), and a variant of n-gram Markov model the bigram hidden Markov model (HMM). The two variants of DRRN models evaluated include forward long short-term memory (LSTM)-RNN and LSTM-RNN with CRF output.
Klasifikace
Druh
O - Ostatní výsledky
CEP obor
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OECD FORD obor
10201 - Computer sciences, information science, bioinformathics (hardware development to be 2.2, social aspect to be 5.8)
Návaznosti výsledku
Projekt
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Návaznosti
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Ostatní
Rok uplatnění
2019
Kód důvěrnosti údajů
S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů