  Automatic Part-of-speech (POS) tagging is a preprocessing step of many
natural language processing (NLP) tasks such as name entity recognition (NER),
speech processing, information extraction, word sense disambiguation, and
machine translation. It has already gained a promising result in English and
European languages, but in Indian languages, particularly in Odia language, it
is not yet well explored because of the lack of supporting tools, resources,
and morphological richness of language. Unfortunately, we were unable to locate
an open source POS tagger for Odia, and only a handful of attempts have been
made to develop POS taggers for Odia language. The main contribution of this
research work is to present a conditional random field (CRF) and deep
learning-based approaches (CNN and Bidirectional Long Short-Term Memory) to
develop Odia part-of-speech tagger. We used a publicly accessible corpus and
the dataset is annotated with the Bureau of Indian Standards (BIS) tagset.
However, most of the languages around the globe have used the dataset annotated
with Universal Dependencies (UD) tagset. Hence, to maintain uniformity Odia
dataset should use the same tagset. So we have constructed a simple mapping
from BIS tagset to UD tagset. We experimented with various feature set inputs
to the CRF model, observed the impact of constructed feature set. The deep
learning-based model includes Bi-LSTM network, CNN network, CRF layer,
character sequence information, and pre-trained word vector. Character sequence
information was extracted by using convolutional neural network (CNN) and
Bi-LSTM network. Six different combinations of neural sequence labelling models
are implemented, and their performance measures are investigated. It has been
observed that Bi-LSTM model with character sequence feature and pre-trained
word vector achieved a significant state-of-the-art result.
