  Part of Speech (POS) tagging is crucial to Natural Language Processing (NLP).
It is a well-studied topic in several resource-rich languages. However, the
development of computational linguistic resources is still in its infancy
despite the existence of numerous languages that are historically and literary
rich. Assamese, an Indian scheduled language, spoken by more than 25 million
people, falls under this category. In this paper, we present a Deep Learning
(DL)-based POS tagger for Assamese. The development process is divided into two
stages. In the first phase, several pre-trained word embeddings are employed to
train several tagging models. This allows us to evaluate the performance of the
word embeddings in the POS tagging task. The top-performing model from the
first phase is employed to annotate another set of new sentences. In the second
phase, the model is trained further using the fresh dataset. Finally, we attain
a tagging accuracy of 86.52% in F1 score. The model may serve as a baseline for
further study on DL-based Assamese POS tagging.
