Abstract Part of speech tagging plays an important role in developing natural language processing software. Part of speech tagging means assigning part of speech tag to each word of the sentence. The part of speech tagger takes a sentence as input and it assigns respective/appropriate part of speech tag to each word of that sentence. In this article I surveys the different work have done about odia POS tagging. ________________________________________________

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A Survey on Odia Part-of-Speech Tagging
Dhabal Prasad Sethi
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Abstract
Part of speech tagging plays an important role in developing
natural language processing software. Part of speech
tagging means assigning part of speech tag to each word of
the sentence. The part of speech tagger takes a sentence as
input and it assigns respective/appropriate part of speech tag
to each word of that sentence. In this article I surveys the
different work have done about odia POS tagging.
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Keywords: Odia pos tagging, morphology, stemmer, natural
language processing.
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I. Introduction
Part of speech tagging is the important thing in natural
language processing.POS tagging is the primary step in the
syntactic analysis of a language. Part of speech tagging is the
process of assigning appropriate part of speech tag for each
word in a sentence. The task of POS tagger is it takes a
sentence as input and assigns unique part of speech tag to each
word of a sentence.POS tagging applications are text to
speech, machine translation etc.POS tagging approach are
generally categories into two: one is supervised and other is
unsupervised. In supervised technique it requires predefined
tagged corpora where an unsupervised technique does not
require a pre tagged corpora. Again supervised and
unsupervised technique can be categories into two e.g. rule
based technique and probability or stochastic based technique.
Rule based technique uses hand written rules along with
linguistic knowledge for POS tagging. Hand written rule
means context rule. On the other hand stochastic approach
requires a corpus. Stochastic tagger use statistics i.e.
frequency or probability to tag the input text. Stochastic
tagging techniques can be two types. Supervised stochastic
tagged technique use the large amount of tagged data for
better accuracy. Unsupervised stochastic technique does not
require a pre-tagged corpus. The disadvantages of stochastic
tagger is that it can come up with sequences of tags for
sentences that are not give accurate results according to
grammatical rules of that language. This aim of the paper is to
survey on various POS tagging work have done about odia
language.
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Author’s Name: Dhabal Prasad Sethi, Lecturer in Computer
Science and Engineering, Government College of Engineering,
Keonjhar, Odisha, India.
_______________________________________________
The rest part of the paper is organized as section II describes
literature survey, section III describes odia part of speech and
odia part of speech tagger, section IV describes POS tagging
methods, section V describes the applications of POS tagging,
and section VI describes the conclusion.
II.Literature Survey
Paper [9] presented a paper named as “A novel approach for
odia part of speech tagging using Artificial Neural Networks
“at international conference on frontiers of intelligent
computing: Theory and computing. They have used the neural
network method to classify the odia sentence in to their
respective POS tagging. The neural network is used to
correcting the errors which consist of multilayer feed-forward
method. The multi-layer neural networks consist of input
layer, the output layer and middle layer or hidden layer. Lastly
they mention morphological analyzer is used to improve the
performance of the tagger. Their tagger has an accuracy of
81% in the test data provided.
Not much work have done about odia pos tagging. It is the
opportunity for researcher to develop pos tagger using
different methods and compare it with existing tagger.
III. Odia Part of Speech
To write, speak and tell any language the person should
known the grammatical knowledge of that language. To learn
the grammar he should know the vowel and consonant along
with their part-of-speech. In English language there are 8part-
of-speech.They are noun, pronoun, adjective, verb, adverb,
preposition, conduction and interjection. Instead of English
term word, in odia it was called pada. The meaningful word
used in sentence is called pada. In odia words/pada are
categories in to 5part-of-speech.They are bisheshya(noun),
sarbanam(pronoun),bisheshana(adjective), abaya(preposition),
kriya(verb). Again bisheshya(noun) is categories into
namabachaka/bya-kti-bachaka bisheshsya, bastu-bachaka
bisheshaya, jati-bachaka bisheshaya, guna-bachaka bisheshya,
kriya-bachaka bisheshya.
Odia pronoun (sarbanam) words are mu, amey, tu, tumey, se,
semaney, apana, kiey, kounasi ,jeumaney, amaemane.
Sarbanam is categories into 6types purusha-bachak,
nirdesha-bachaka, anishabachaka, sambandha bachaka,
prasna-backaka, nija-bachaka. Bisheshana(adjective) is
different types bisheshya ra bisheshana, bidheya bisheshana,
sarbanam ra bisheshana, bisheshana ra bisheshana, kriya
bisheshana.Preposition(abaya) is different types padanoyi
abaya, parimana suchaka, samaya suchaka, sambhabana
suchaka, nirdeshathrak, bipsarthak, bikalpa-thrak, sadrushya
bachaka, sanmati suchaka, sambodhana suchaka, nischaya
bodhaka, bhaba bachaka, hetu bodhaka,

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sabdhanukari.Verb/kriya are different types.They are
sakarmaka kriya, akarmaka kriya, samapika kriya, asmapika
kriya, mishra kriya.
a. Odia Part of Speech Tagger
The part of speech tagger takes a sentence or word as input
and it gives the respective part of speech tag for each word of
the sentence. In odia sentence, the POS tagger works as
[Input: Cuttack Odishara Puruna Rajadhani
Output: Cuttack/N Odisha/N Puruna/ADJ Rajadhani/N ]
Like English and other language, odia language has the
ambiguity words. In odia the word akhi(eye) can give
different meaning in different sentence. Example: TARA
AKHI BAHUTA SUNDER and another sentence SE SEI
KAMATTIKU KARIBAKU AKHEICHI. In the first sentence
AKHI is noun (bishesya) and in the second sentence
AKHEICHI is verb (kriya).Some words have different part of
speech category. In this case problem arises how to categories
the respective part of speech of that word.
IV.POS Tagging Methods
1) Rule based Technique
In rule based technique the pos tagger depends on lexicon or
dictionary to get possible tag for each word in the sentence.
Hand written rules are applied when a word has more than on
tag set. Disambiguation is done by analyzing the linguistic
features of the words, its following word, its preceding word
and other aspect. Example, if an ambiguous/unknown word A
is preceded by a determiner and followed by a noun, tag it as
an adjective. det - A - n = A/adj The advantages of rule based
technique are that it stores less information and a small
meaningful rule is applied.
2) Stochastic Approach
Stochastic approach or probability approach uses training
corpus to pick the probable tag for a word. Examples of
stochastic taggers are hidden Markova model, transformation-
based learning, maximum-entropy based modeling, support
vector machines and neural network.
a)Hidden Markov Model
HMM have been used in various nlp applications. The HMM
consist of states called tags, an output alphabet e.g. words,
initial states e.g. beginning states, state transition probabilities,
symbol. The parameters of HMM are taken from corpus. The
HMM will be used to assign the best probable tag to the words
of the input sentence by calculating the forward and backward
probabilities of tags along with sequence provided as input.
The tagger is calculated as P (ti|wi) =p (ti|ti-1).p (ti+1|ti).p (wi|ti).
Here p (ti|ti-1) is the probability of accurate tag given the
previous tag. P (ti+1|ti) is the probability of the future tag given
the current tag. p(ti|ti-1)is calculated as freq(ti-1,ti)/freq(ti-1).Each
tag transition probability is computed by calculating the
frequency count of two tags between together in the corpus
divided by the frequency count of the previous tag.
b) Transformation-based Tagging
The transformation based approach is similar to the rule based
approach in the sense that it depends on a set of rules for
tagging. Transformation-Based Tagging, sometimes called
Brill tagging, is an instance of the Transformation-Based
Learning (TBL) approach to machine learning (Brill, 1995)
and draws inspiration from both the rule-based and stochastic
taggers. Like the rule-based taggers, TBL is based on rules
that specify what tags should be assigned to a particular word.
But like the stochastic taggers, TBL is a machine learning
technique, in which rules are automatically induced from the
data [4].
c) Maximum-Entropy based Tagging
The aim of the maximum entropy tagging is to design a model
with maximum entropy. The term maximum entropy means
maximum randomness. It acquires the good properties of
markov model tagging and transformation-based learning. The
outputs of maximum entropy tagging are tags and their
probabilities. The maximum entropy model uses a single
probability model which consists of training data. The
probability model is taken over a space H*T, where H is the
set of environment in which a word appears and T is the set of
possible tags. This model specifies a set of features from
environment for tag prediction. Example, the environment is
specified as: hi={wi,wi+1,wi+2,wi-1,wi-2,ti-1,ti-2}.where h is the
environment for the word and t for tag, i for index.
d) Support Vector Machines
Support vector machine is a supervised machine learning
technique which is used for classification and regression.
Input data is classified in to two set of vector in n-dimensional
space. A support vector machine creates a separating hyper
plane in between the space, one which maximizes the margin
between the two data set. To construct the margin, two
parallel hyper planes are constructed.
Classification based pos tagger compute the most likely pos
tag for each word individually. The task of support vector
machine is to predict the pos tag (class) of a word based on a
set of features describing the word and the context. Other
classification based algorithm are applied in pos tagger are
neural network and decision tree.
e) Neural network
In neural network features of input data are encoded so that it
requires less training time. The neural network/multi layer
perception network consist of at least three layers, input layer,
hidden layer or middle layer and output layer where each layer
consist of elementary processing unit. The elementary
processing unit consists of artificial neurons and incorporates
a nonlinear activation function, the sigmoid function. An
active value is associated with each processing elements. The
MLP network is fully connected i.e. all neurons of one layer is
connected to every neurons in the adjacent layer by weighted
direct links. The neurons of the input layer receive input from
external environment while neurons of the hidden layer
participate in the projection. The neurons of the output layer
participate in the separate classes.

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V. Applications of POS tagging
The applications of pos tagging are speech synthesis and
recognition, information extraction and retrieval, partial
parsing, machine translation, lexicography.
VI. Conclusion
Pos tagging is the important part in natural language
applications. In this article I explain the ambiguity problem of
Odia part of speech words and different methods are generally
used in POS tagging.
References
[1]current state of the art POS tagging for Indian languages-a
study by shambhavi B.R from department of CSE,R V
College of engineering,banglore and dr ramakanth kumar P
from department of ISE,R V C ollege of Engineering,banglore
at international journal of computer engineering and
technology,volume1,number1 may-jun2010. Page250-260
[2]HMM based POS tagger for Hindi by Nisheeth joshi,
Hemant Darbari and Iti Mathur from Department of computer
science Banasthali University and Center for Development of
Advanced Computing, Pune, Maharashtra, India
[3] A Hybrid Model for Part-of-Speech Tagging and its
Application to Bengali by Sandipan Dandapat, Sudeshna
Sarkar and Anupam Basu at transactions on engineering,
computing and technology v1 december 2004 issn 1305-5313
[4] Parts Of Speech Tagging for Indian Languages: A
Literature Survey by Antony P J Research Scholar ,
Computational Engineering and Networking (CEN), Research
Centre, Amrita Vishwa Vidyapeetham University,
Coimbatore, India and Dr. Soman K P Professor and Head,
Computational Engineering and Networking (CEN), Research
Centre, Amrita Vishwa Vidyapeetham University,
Coimbatore, India at International Journal of Computer
Applications (0975 – 8887) Volume 34– No.8, November
2011
[5] a simple rule based part of speech tagger by Eric
Brill,department of computer science,university of
Pennsylvania,Philadelphia,Pennsylvania
[6]part of speech tagging using neural network by Ankur
parikh in international conference on natural language
processing(ICON-2009)
[7] hmm based pos tagger and rule-based chunker for bengali
sivaji bandyopadhyay , asif ekbal, debasish haldercomp. sc. &
engg. deptt.jadavpur university,kolkata.
[8] part of speech tagger for assamese text navanath saharia,
dhrubajyoti das, utpal sharma, jugal kalita ,department of
cse,tezpur universityindia – 784028
[9]“A novel approach for odia part of speech tagging using
Artificial Neural Networks “at international conference on
frontiers of intelligent computing: Theory and computing by
Biswa Ranjan Das and Srikanta Patnaik from department of
computer science and information technology, institute of
technical education and research, Siksha ‘O’ Anusandhan
University, BBSR,INDIA .
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Biography
Dhabal Prasad is currently working as a lecturer in CSE at
GCE, Keonjhar, odisha. He has presented 5 numbers of
papers in different journals. This is his 6th
number at
international level. His research area of interest is Natural
language processing, Information retrieval and Software
engineering.
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