Mazida Ahmed, Kuwali Talukdar, Parvez Boruah, Prof. Shikhar Kumar Sarma, Kishore Kashyap. Proceedings of the Eighth Conference on Machine Translation. 2023.

Religious texts are known to be with specific patterns of writing, and also involve specific vocabularies. These are also known to follow specific style of writing. Thereby these texts are enriched with typical semantic and syntactic characteristics, demanding special attention for Natural Language Processing (NLP) tasks. This research paper focuses on the application of Deep Learning (DL) techniques for Parts of Speech (PoS) tagging focusing on Assamese language religious texts. We have created a specialized dataset comprising approximately 11,000 sentences extracted from various sources including web crawling and filtering religious texts from existing corpora. The dataset was manually validated by linguists to ensure accuracy, errors, and corrections required. A performance matrix was constructed to analyze the performance of the initial tagging using a pre-existing DL-based model trained for Assamese Universal Parts of Speech (UPoS) tagger. Following this, we utilized a subset of the dataset for manual evaluation, and the validated dataset is then considered as a gold standard training dataset for training other DL models using GRU, RNN and Bidirectional LSTM (BiLSTM) architectures. Training accuracies were recorded and presented, demonstrating the effectiveness of the proposed approach. Accuracies, Precision, and Recall were recorded for all the three Models. F1 scores also have been calculated. Comparison of training and testing accuracies are depicted with performance graphs.

Shikhar Kr. Sarma, Ratul Deka, Bhatima Baro, Vaskar Deka, Umesh Deka, Mirzanur Rahman, Sarmah Satyajit, Kuwali Talukdar, Kishore Kashyap. Proceedings of the 13th Global Wordnet Conference. 2025.

This paper describes in detail the Universal Parts of Speech (UPoS) tagged dataset for the Assamese language. PoS tagged dataset in a language is crucial for experimenting and creating resources for various Natural Language Processing (NLP) and AI research. With the growing usage of Universal Dependency standards, tagged dataset with Universal PoS tags are becoming very much essential for contemporary experiments in the NLP community. NLP research in Assamese, and Indo-Aryan language, is relatively new, and the language is considered a Low Resource language. The dataset of UPoS tagged Assamese text is created with an aim of contributing towards enriching this low resource language for NLP and AI tasks. The size of the dataset is 283506 tokens of Assamese vocabulary, against total 20280 sentences, tagged with 17 standard UPoS tags of core lexical categories. The raw data are taken from an open-source corpus originally tagged with BIS tagset. The original size of 453457 tokens against 29504 sentences, after subjected to data filtering, was reduced to this clean resource of 283506 tokens. Lexical categories mapping is done with linguistic expertise, from BIS to UPoS tagsets. Mapped pattern was used for a first-level conversion of BIS tags to UPoS tags. Linguistic validation is also performed with linguistic experts and inter annotator agreement/disagreements were recorded. Second level validation resulted in deciding on the agreement, producing the final version of the dataset. This Assamese UPoS tagged dataset is the first of its kind with UPoS annotations and shall serve a wider Assamese NLP research community for model training using Machine Learning/Deep Learning Techniques.