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Leveraging Bidirectionl LSTM with CRFs for Pashto tagging

Domaine:

natural language processing

Type de record:

paper
Créateur:
FarOnaJaw
Éditeur:
Res
Hôte:
Abstract Part-of-speech tagging plays a vital role in text processing and natural language understanding. Very few attempts have been made in the past for tagging Pashto Part-of-Speech. In this work, we present LSTM based approach for Pashto part-of-speech tagging with special focus on ambiguity resolution. Initially we created a corpus of Pashto sentences having words with multiple meanings and their tags. We introduce a powerful sentences representation and new architecture for Pashto text processing. The accuracy of the proposed approach is compared with state-of-the-art Hidden Markov Model. Our Model shows 87.60\% accuracy for all words excluding punctuations and 95.45\% for ambiguous words, on the other hand Hidden Markov Model shows 78.37\% and 44.72\% accuracy respectively. Results show that our approach outperform Hidden Markov Model in Part-of-Speech tagging for Pashto text.

Visit

doi.org

Tasks

part of speech tagging

Licenses

https://creativecommons.org/licenses/by/4.0/

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