A COMPREHENSIVE ANALYSIS OF STEMMERS AVAILABLE FOR INDIC LANGUAGES

International Journal on Natural Language Computing (IJNLC) Vol. 5, No.1, February 2016
DOI: 10.5121/ijnlc.2016.5104 45
A COMPREHENSIVE ANALYSIS OF STEMMERS
AVAILABLE FOR INDIC LANGUAGES
Harshali B. Patil, B. V. Pawar, and Ajay S. Patil
School of Computer Sciences, North Maharashtra University, Jalgaon, India
ABSTRACT
Stemming is the process of term conflation. It conflates all the word variants to a common form called as
stem. It plays significant role in numerous Natural Language Processing (NLP) applications like
morphological analysis, parsing, document summarization, text classification, part-of-speech tagging,
question-answering system, machine translation, word sense disambiguation, information retrieval (IR),
etc. Each of these tasks requires some pre-processing to be done. Stemming is one of the important building
blocks for all these applications. This paper, presents an overview of various stemming techniques,
evaluation criteria for stemmers and various existing stemmers for Indic languages.
KEYWORDS
Stemming, light weight, affix removal, HMM, n-gram, Indian languages
1. INTRODUCTION
In 21st
century the e-data in regional languages appearing on internet increased drastically. But
due to the unavailability of NLP tools for these languages, access to this data is limited. The
scarcity of tools has attracted the attention of researchers and industry persons towards the
development of efficient systems for IR, text summarization, opinion mining, clustering,
classifications etc., for regional languages. The inflected nature of a language poses several
challenges for the automated processing of natural language data. In an information retrieval
system that does not use any word normalization, search results are directly affected as many
relevant documents are missed during the retrieval process. For instance, in case of the search
query “stemmer”, the system will not return documents containing “stemming” or “stem” if the
documents do not contain the term “stemmer”. Term conflation is the solution to these types of
problems. In many natural languages several words sharing the same morphological variant (root)
can be related to the same topic. Stemming is one of the popular term conflation techniques used
as a preliminary step in many natural language processing tasks. In linguistics stem is the form
that unifies the elements in a set of morphologically similar words. Stemming is the process
which determines the stem of the given word. The goal of a stemming algorithm is to reduce
variant word forms to a common morphological root, called stem [1]. India is a multilingual
country where there are 22 official languages which belongs to 4 different families of languages.
Among these 22 languages, 15 languages belong to Indo-Aryan, 4 are Dravidian, 2 are Tibeto-
Burman, and 1 belongs to Munda family.
This paper presents a comprehensive analysis of stemmers available for Indic languages. The
paper is organized as follows: related work is provided in section 2. Section 3 describes stemming
process in detail as well as existing stemmers for Indic languages and Section 4 concludes the
paper.

46
2. RELATED WORK
Stemming is a well known research problem; but in early days of stemming it was studied only
for English language. Lovin’s stemmer is one of the oldest stemmer developed for English using
context sensitive longest match technique. Other most notable stemmers for English includes:
Porter’s stemmer, Dawson stemmer, Paice and Husk stemmer. Poter’s stemmer has became the
de-facto standard for English language. Some non-English stemmers were presented during 1990-
2000, but study related to stemming for Indic languages has been started after 2000. The
subsequent section discusses the work done related to stemmer development for Indic languages.
Most of the work related to stemmer development has most recently initiated. Sarkar et.al (2012)
surveyed stemmers for Bengali and has inferred that rule-based stemmers would be more suitable
for Bengali, whereas suffix strippers may not be sufficient for Bengali. The author also concludes
that POS tagged data and lexicon may improve performance in Bengali stemmer and accuracy
based evaluation techniques should be appropriate for measuring stemmer performance [21].
Lakshmi et. al. (2014) reviewed literature related to stemming algorithms for Indian and Non-
Indian languages and found that there is a need to develop a language independent stemmer for
all languages [27]. Madhurima et.al (2013) analyzed popular stemming algorithms supporting
information retrieval system and has shown that no perfect stemmer has been designed so far to
match all the requirements [22]. Kasthuri et. al (2014) comprehensively analyzed stemming
algorithms for Indian and non-Indian languages, but the analysis focuses on only recent stemmers
developed during 2010 to 2014 [8]. Bijal et. al overviewed stemming algorithms for Indian and
non-Indian languages and discussed 9 stemmers related to Indian languages [24]. Rakholia et. al.
(2014) presented comparative analysis of different stemmers and character recognition algorithms
for Indian Gujarati script where the author discussed the stemmers available for Gujarati language
only [25]. Sethi el.al (2014) presented a literature survey related to stemming algorithms for Odia
language [26]. Patil et.al (2014) presented a part-of-speech tagger for Marathi language using
limited training corpora and obtained 78.82% accuracy with the rule-based technique [41]. The
study related to developing links of compound sentences for parsing through Marathi link
grammar parser was carried out by Vaishali Patil et. al (2014) [42,43]. Nita Patil et.al (2016)
surveyed the name entity recognition (NER) systems with respect to Indian and foreign languages
and concludes that very less work on NER is reported for Indian languages like Marathi and
Guajarati [44]. Juhi et.al (2013) improved the quality of Gujarati-Hindi machine translation
through part-of-speech tagging and stemmer assisted transliteration and achieved 93.09% overall
efficiency of the transliteration scheme [45]. Kridanta analysis for Sanskrit has been done by
Murali et.al (2014) and achieved 92.75% and 95.37% precision and recall for Kridanta analyzer
[46].
3. STEMMING
The following section presents the detail information about stemming in terms of techniques used
to develop stemmers, various types of errors generated while stemming, evaluation criteria for
stemmers, and the existing stemmers for Indic languages. The major advantages and limitations
provided by stemming for information retrieval are as given below:
Advantages of stemming
• Use of stemmers in IR decreases index size because for all the terms that belongs to single
conflation class are reduced to single term. [16, 20].
• Reduction in index ultimately reduces the storage space required to store the inverted index
file. file [16, 47, 48].

47
• Instead of using term variation if the stem is used for IR it increases the recall of retrieval
systems. [2, 20].
• It is used as an important component for pre-processing in many applications.
Limitations of stemming
• Exceptional cases are also grouped together e.g – university and universal – univers
• Sometimes decreases the retrieval performance
3.1. Classification of stemming techniques
The stemmers are broadly classified into two types: manual and automatic[48]. Manual stemmers
stems the terms manually while automated stemmers can be developed by using various
techniques. Simplest affix removal to a complicated technique like n-gram or Hidden Markov
Model (HMM) has been used for development of stemmers for various languages. Fig. 1
classifies the major stemming techniques used for stemmer development.
Figure 1. Some automatic stemming techniques
3.1.1. Affix removal Technique
These types of stemmers are also called as rule-based stemmers or suffix stripper. This is one of
the oldest and simplest techniques used for stemmer development. These types of algorithms uses
list of suffixes and with each suffix the criteria under which it may be removed from a word to
leave a valid stem. The affix removals based on rules are either done based on longest match
basis or in iterative manner. Fig 2 shows the procedure for affix removal stemmers. Some Indic
stemmers developed using this technique includes [3, 5, 9, 12, 38, 39].
Figure 2. Affix removal stemming procedure
StemStemming
Affix list Stripping
criteria
Term

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The sample rule related to English affix removal / rule-based stemmer is as given below:
3.1.2. Table / Dictionary lookup
In this technique a table of corresponding terms along with their stem is used. The stemming is
done by searching the corresponding term in the table and retrieving the stem related to that term
[48]. This technique is not popular for stemmer development due to limitations related to it: like
dictionary of term-stem are not available for many languages, the accuracy totally depends on the
size of dictionary, and dictionary are domain dependent; but this technique can be combined with
rule-based technique to develop hybrid stemmers and increase the stemming accuracy. Fig. 3
shows the process of stemming with help of dictionary / table lookup approach.
Figure 3. Stemming procedure for dictionary / table lookup
3.1.3. Statistical Techniques
These types of stemmers uses some statistical measure for stemmer development like n-gram,
HMM, clustering-based method, corpus-based method. Some of them are briefly explained in the
following section. In this approach first the system is trained with the large corpus and then
inflected forms are submitted to the trained module for stemming.
N-gram:
N-gram is a set of n consecutive characters extracted from a word. In this technique the similar
words will have a high proportion of n-grams in common. The n-gram based stemmer calculates
the association measure between the pair of terms based on shared unique diagrams. Then
similarity measures are determined for all pairs of terms in the database, forming a similarity
matrix. After that terms are clustered using single link clustering. [40]
HMM:
The HMM is a finite sequence of states, and a set of transitions between states that are taken
based on the input observations. Each character comprising a word is considered as a state. All
possible states are divided into two groups (roots and suffixes) Word building process are defined
by the transitions between states. [29]
Rule :- IES -> I
E.g. :- Ponies -> Poni
Corpus
Documents
Dictionary Stemming
Stemmed
words
List of words
to be
stemmed

49
Clustering-based:
In these types of stemmer first the Equivalence classes are discovered. Then a set of string
distance measures are defined. The lexicon for a given text collection is clustered using complete
linkage clustering technique to identify these equivalence classes. For some infrequent cases the
author uses some post-processing for each cluster. [2]
Corpus-based:
First the set of potential suffixes are formed. Then the equivalence classes are generated by
combining the common prefix and potential suffix information. Then the common prefix and
potential suffixes help to recognize the better equivalence classes through mutual agreement. [14]
3.1.4. Hybrid
When any two or more approaches are combined for stemmer development then the stemmer
becomes hybrid. Most hybrid stemmers were developed by combining lookup table with affix
removal. They overcome the disadvantages of one technique by combining it with other; for
example the lookup table based stemmer cannot able to stem if term is not present in the table so
when this type of stemmer is combine with affix removal then it can be able to stem the term
which are not present in dictionary. . The work related to hybrid stemmers for Indic languages has
been reported by [4, 6, 15, 29, 30, 37].
3.2. Comparison of stemming techniques
Table 1 compares the stemming techniques (rule based, dictionary lookup, statistical and hybrid)
in terms of advantages and limitations.
Table 1. Comparison of stemming techniques.
3.3. Evaluation criteria for stemmers
Stemmer is used as a pre-processing component for various NLP applications. There are several
criteria that are used for evaluation of stemmers. Some of them include: correctness, retrieval
effectiveness, and compression performance [48]. Generally stemmer is evaluated based on
accuracy provided by it for stemming. Paice has given a method to evaluate stemmers based on
two types of error counting [33]. Under-stemming errors and over-stemming errors are used to
Technique Advantages Limitations
Rule-based Easy to implement, requires less memory,
dictionary not required and
fast
Rule preparation needs to be done
manually, Over-stemming and under-
stemming error rate is high.
Dictionary
lookup
has capability to work with exceptional
cases, under and over-stemming error rate
is reduced and is fast
Dictionary / table is not available,
storage and retrieval overhead,
accuracy depends on dictionary size.
Statistical
Technique
Applicable for wide variety of languages Requires large corpus to train the
system, takes more processing time,
requires significant amount of memory.
Hybrid Combination of more than one technique,
overcomes the drawbacks of each other.
If dictionary lookup is combined then
extra overhead for storage.

50
evaluate the stemmer. Section 3.4 discusses these two types of errors. In IR few studies has
evaluated stemmers as a method for index compression while some discuss the improvement in
precision and recall for IR.
Frakes and Fox has given the stemmer strength metrics. [31,32] The degree to which a stemmer
changes words that it stems is called stemmer strength. Some ways to measure stemmer strength
are:
• The number of words per conflation class.
• Index compression factor.
• The number of words and stems that differ.
• The mean number of characters removed in forming stems.
• The median and mean modified Hamming distance between words ad their stems.
3.4. Types of errors in stemming
Generally two types of errors i.e over-stemming and under-stemming errors are generated while
stemming. [33]
3.4.1. Over-stemming
Over-stemming errors refers to the words that should not be grouped together by stemming but
they are grouped together. These types of errors will affect on precision of IR.
3.4.2. Under-stemming
Under-stemming refers to words that should be grouped together by stemming, but that are not
grouped together. These type of errors tend to decrease the recall in an IR search.
3.5. Existing stemmers for Indic languages
Significant work related to stemmer development has been done for non-Indic languages like
English, Arabic, etc. whereas stemming work is in progress for Indic languages. The work related
to stemmer development for various Indic languages has started after 2000. Till 2010, not much
work related to stemmer development has been reported for Indic languages. India is a
multilingual country where there are 22 official languages. Among these languages stemmers are
available for 13 Indic languages. Some language independent stemmers are also available. Table
2 presents the existing stemmers for Indic languages.
Table 2. Existing stemmers for Indic languages.
Sr.
No.
Language
Author &
Reference
Year Approach Accuracy / Results
1
Hindi
Ramanathan
el al [9]
2003
Suffix
removal
Results are favourable and
can be used effectively in
information retrieval
2 Pande et. al
[36]
2008 Unsupervised
89.90%
3
Dolamic
et.al [11]
2010
Light and
aggressive
Improvements over no
stemming: 19.3% with light
stemmer, 27.6% with
aggressive stemmer

51
4 Mishra et. al
[37]
2012
Hybrid 91.59%
5 Gupta [5] 2014 Rule –based 83.65%
6
Bengali
Sarkar et. al
[39]
2008
Rule-based 89% and above
7 Zahurul et.
al [10]
2008
Lightweight 90.80%
8
Dolamic
et.al [11]
2010
Light and
aggressive
stemmer , 17.7% aggressive
stemmer
9 Das et. Al
[12]
2011 Rule-based
0.4748 (Mean Average
Precision)
10
Punjabi
Kumar et. al
[13]
2010
Brute force
algorithm
80.73%
11 Gupta et.al
[38]
2011 Rule-based
87.37%
12 Joshi et.al
[6]
2014 Hybrid
95.6%
13
Marathi
Dolamic et.
al [11]
2010
Light and
aggressive
stemmer , 41.6% with
aggressive stemmer
14
Majgaonkar
et. al [3]
2010
Rule -based
and
unsupervised
80.7% for rule-based
82.5% for unsupervised
15
Tamil
Ramchanrda
n et. al [19]
2012
Suffix
stripping
84.79%
16 Thangarasu
et. al [20]
2013
Light
stemming
83.28%
17
Odia
Chaupattnai
k et. al [17]
2012
Suffix
stripping
88%
18 Sethi [18] 2013 Light weight 85%
19 Assamese
Saharia et.
al. [29]
2013
Hybrid (rule-
based and
HMM)
92%
20
Gujarati
Patel el.al
[4]
2010 Hybrid
67.86%
21
Suba et.al
[30]
2011
Hybrid and
rule-based
90.70% for hybrid
70.70% for rule-based
22 Kannada Bhat [16] 2013
Statistical
technique
88.82%
23 Kokborok
Patra et. al
[23]
2012 Rule –based
80.02% for minimum suffix
stripping, 85.13% for
maximum suffix stripping
24 Malayalam
Prajitha et.
al [35]
2013
Suffix
stripping
Computationally inexpensive
and domain independent.
25 Manipuri
Meitei et.
al.[15]
2015 Hybrid
86.29%
26 Telugu Kumar[34] 2013 Unsupervised 85.40%

52
27 Urdu
Gupta
et.al[28]
2013 Rule-based
86.50%
28
Language
independent
Majumder
et. al [2]
2007
Clustering -
based
Improvement in the recall for
information retrieval
29
Paik et. al
[14]
2011
Statistical
technique
Outperformance observed
over the well known rule-
based stemmers for all
languages under study
30 Husain [7] 2012
Length-based
and
frequency-
based
unsupervised
approach
Length based 79.63% (Urdu),
82.6% (Marathi), frequency
based 84.27% (Urdu), 63.5%
(Marathi)
Various techniques were used for the evaluation of stemmers as mentioned in section 3.3. It is
observed that the percentage accuracy provided by the stemmer was generally used to evaluate
the stemmers. Form the literature review related to Indic languages stemmer development it is
found that there is a difference in the accuracy levels provided by various types of stemmers.
Table 3 presents the percentage accuracy obtained by using various approaches for Indic
languages stemmer development.
Table 3. Accuracy ranges for stemming techniques
Sr. No. Technique
Accuracy range
(%)
1 Rule –based 80.02 – 89
2 Statistical 63.5 - 89.9
3 Hybrid 67.86 - 95.6
From table 3 it is observed that the maximum accuracy level provided by rule-based and
statistical techniques are nearly same but there is lots of difference between the minimum
accuracy levels achieved by both of these techniques. The maximum accuracy reported for Indic
languages are obtained by the use of hybrid technique.
4. CONCLUSION
Stemmer is an important and basic component in many natural language processing applications.
The accuracy of the stemmer strongly affects the results of the system in which it is used.
Various stemmer development techniques are being explored and studied for different languages
across the world. This paper surveyed the stemmers available for Indic languages. From literature
survey it is observed that the work related to stemmer development for some of the Indic
languages like Mizo, Santhali, etc has not been reported. The rule-based and light stemming
techniques are widely used for stemmer development for languages like Hindi, Marathi, Tamil,
etc. Hybrid approaches are also used by some researchers to avoid limitations of using single
technique. Hybrid technique has reported more accuracy than the other techniques for some of the
Indic languages. Though some work related to Indic stemmer development has been reported still
much work needs to be done. The techniques like dictionary lookup or hybrid approach needs to
be evaluated for the languages like Marathi, Hindi, etc. We intend to develop a stemmer for
Marathi language that will be used for efficient information retrieval. The survey of the existing

53
techniques will be helpful in deciding the applicability of these techniques for Marathi stemmer
development.
ACKNOWLEDGEMENTS
The authors are thankful to University Grants Commission (UGC), New Delhi, India, for
financial assistance for the research study under the scheme of Special Assistance Programme
(SAP) of Departmental Research Support (DRS) Phase I.
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