Development of a Dataset and a Deep Learning Baseline Named Entity Recognizer for Three Low Resource Languages: Bhojpuri, Maithili and Magahi

no code implementations • 14 Sep 2020 • Rajesh Kumar Mundotiya, Shantanu Kumar, Ajeet kumar, Umesh Chandra Chaudhary, Supriya Chauhan, Swasti Mishra, Praveen Gatla, Anil Kumar Singh

The lower baseline F1-scores from the NER tool obtained by using Conditional Random Fields models are 96. 73 for Bhojpuri, 93. 33 for Maithili and 95. 04 for Magahi.

Machine Translation named-entity-recognition +3

Linguistic Resources for Bhojpuri, Magahi and Maithili: Statistics about them, their Similarity Estimates, and Baselines for Three Applications

no code implementations • 29 Apr 2020 • Rajesh Kumar Mundotiya, Manish Kumar Singh, Rahul Kapur, Swasti Mishra, Anil Kumar Singh

Corpus preparation for low-resource languages and for development of human language technology to analyze or computationally process them is a laborious task, primarily due to the unavailability of expert linguists who are native speakers of these languages and also due to the time and resources required.

Chunking POS +1