A deep learning approach to building a framework for Urdu POS and NER

What is it about?

This publication presents a framework for Urdu Named Entity Recognition and Part of Speech Tagging and subsequently compares the proposed framework with several other models to assess its effectiveness.

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Photo by Ferenc Almasi on Unsplash

Photo by Ferenc Almasi on Unsplash

Why is it important?

The study of Named Entity Recognition and Part of Speech Tagging in Urdu language is important for several reasons: 1. Urdu is a widely spoken language in South Asia and has a rich cultural heritage. Therefore, the development of language processing tools for Urdu can enable the preservation and dissemination of its cultural heritage. 2. Urdu is an Indo-European language with a unique script and grammar, making it challenging to develop language processing tools. The development of models for NER and POS tagging in Urdu can contribute to the advancement of language processing technology. 3. The availability of accurate NER and POS tagging tools can greatly enhance the quality of machine translation, information retrieval, and text classification systems for Urdu. This can lead to improved access to information and resources in Urdu, which is essential for supporting the linguistic and cultural diversity of the region. In summary, this study of NER and POS tagging in Urdu is important for promoting linguistic and cultural diversity, advancing language processing technology, and improving access to information and resources in the language.

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