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.

本文介绍了基于条件随机场和深度学习的方法开发 Odia 词性标注器。通过实验，发现使用带字符序列特征和预训练词向量的 bi-LSTM 模型可以达到显著的最新成果。

基于统计和深度学习的奥迪亚语词性标注

Part-of-Speech Tagging of Odia Language Using statistical and Deep Learning-Based Approaches

Shallow parsing is an essential task for many NLP applications like machine
translation, summarization, sentiment analysis, aspect identification and many
more. Quality annotated corpora is critical for building accurate shallow
parsers. Many Indian languages are resource poor with respect to the
availability of corpora in general. So, this paper is an attempt towards
creating quality corpora for shallow parsers. The contribution of this paper is
two folds: creation pos and chunk annotated corpora for Odia and development of
baseline systems for pos tagging and chunking in Odia.

本文旨在为 shallow parsing 构建 Odia 语言的 pos 标记和 chunk 标记标注语料库，并开发出针对该语言的基线系统。