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Computational Morphology and Natural Language Parsing for Indian Languages: A Literature Survey

Antony P J and Dr. Soman K P

AbstractComputational Morphology and Natural Language Parsing are the two important as well as essential tasks required for a number of natural language processing application including machine translation. Developing well fledged morphological analyzer and generator (MAG) tools or natural language parsers for highly agglutinative languages is a challenging task. The function of morphological analyzer is to return all the morphemes and their grammatical categories associated with a particular word form. For a given root word and grammatical information, morphological generator will generate the particular word form of that word. On the other hand Parsing is used to understand the syntax and semantics of a natural language sentences confined to the grammar. This literature survey is a ground work to understand the different morphology and parser developments in Indian language. In addition, the paper also deals with various approaches that are used to develop morphological analyzer and generator and natural language parsers tools .

Index TermsSuffix stripping, Lexicon, Synthesizer, Natural Language Processing, Syntactic Parsing, Grammar

Refinement Process, Support Vector Machine, Context Free Grammar

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The literature shows that development of morphological analysis and generation as well as natural language parsing work has been successfully done for languages like English, Chinese, Arabic and European languages using various approaches from last few years. Literature shows that there are a very few number of attempts for Indian languages and still these are an ongoing process. The overall structure of this paper is broadly divided into two sections. The first section deals with the various approaches and different developments in morphologi- cal analyzer and generator (MAG) tools for Indian lan- guages. On the other hand the second section gives a literature survey on Indian language natural language parsing.


The morphological structure of an agglutinative lan- guage is unique and capturing its complexity in a ma- chine analyzable and generatable form is a challenging job. Analyzing the internal structure of a particular word is an important intermediate stage in many natural lan- guage processing applications especially in bilingual and multilingual MT system. A Morphological analyzer is used to analyze the internal structure of the words of a language. On the other hand a morphological generator does exactly the reverse of it i.e. given a root word and grammatical information morphological generator will generate the particular word form of that root word. The role of morphology is very significant in the field of NLP, as seen in applications like MT, question- answering (QA) system, IE, IR, spell checker, lexicogra-
phy etc. So from a serious computational perspective the creation and availability of a morphological analyzer for a language is important. To build a MAG for a language one has to take care of the morphological peculiarities of that language, specifically in case of machine translation. Some peculiarities of language such as, the usage of clas- sifiers, excessive presence of vowel harmony etc. make it morphologically complex and thus, a challenge in natu- ral language generation (NLG). The first section of this chapter discuss varies approaches that used for building morphological analyzer and generator tool for Indian languages. The second section gives a brief explanation about different morphological analyzer and generator developments for Indian languages.

2.1 Morphological Analyzer and Generator Ap-


In general there are several approaches attempted for developing morphological analyzer. In 1983 Kimmo Koskenniemi developed a two-level morphology ap-
proach, where he tested this formalism for Finnish lan-
guage [1]. In this two level representation, the surface
level is to describe word form as they occur in written text and the lexical level is to encode lexical units such as stem and suffixes. In 1984 the same formalism was ex- tended in other languages such as Arabic, Dutch, Eng-
lish, French, German, Italian, Japanese, Portuguese, Swedish, Turkish and developed morphological analyz- ers successfully. In the same time a rule based heuristic analyzer for Finnish nominal and verb forms was devel- oped by Jappinen [2]. In 1996, Beesley [3] developed an Arabic finite state transducer for MA using Xerox finite state transducer (XFST), by reworking extensively on the lexicon and rules in the Kimmo-style. At 2000, Agirve

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introduced a word–grammar based morphological ana- lyzer using the two- level and a unification- based for- malism for a highly agglutinative language called Bas- que [4]. Similarly using XFST, karine made a Persian MA [5] in 2004 and Wintner came up with a morphological analyzer for Hebrew [6] in 2005. Oflazer Kamel devel- oped a Finite State Machine (FSM) based Turkish mor- phological analyzer. In 2008, using the syllables and uti- lizing the surface level clues, the features present in a word are identified for Swahili (or Kiswahili) language by Robert Elwell.
There are many language dependent and independent approaches used for developing morphological analyzer and generator [7]. These approaches can also be classi- fied generally into corpus based, rule based and algo- rithmic based. The corpus based approach, where a large sized well generated corpus is used for training with a machine learning algorithm. The performance of the system will depends on the feature and size of the cor- pus. The disadvantage is that corpus creation is a time consuming process. On the other hand, rule based ap- proaches are based on a set of rules and dictionary that contains root and morphemes. In rule based approaches every rule depends on the previous rule. So if one rule fails, it will affect the entire rules that follow it. When a word is given as an input to the morphological analyzer and if the corresponding morphemes are missing in the dictionary then the rule based system fails. Literature shows that there are number of successful morphological analyzer and generator development for languages like English, Chinese, Arabic and European languages using these approaches [8]. Recent development in Indian lan- guage NLP shows that many morphological analyzer and generators are created successfully using these ap- proaches. A brief description of most commonly used approaches is as follow:

Corpus Based Approach: In case of corpus based approach,

a large sized well generated corpus is required for train-
ing. Any machine learning algorithm is used to train the
corpus and collects the statistical information and other
necessary features from the corpus. The collected infor-
mation is used as a MAG model. The performance of the system will depends on the feature and size of the cor- pus. The disadvantage is that corpus creation is a time consuming process. This approach is suitable for lan-
guages having well organized corpus.

Paradigm Based Approach: For a particular language, each

word category like nouns, verbs, adjectives, adverbs and
postpositions will be classified into certain types of pa-
radigms. Based on their morphophonemic behavior, a
paradigm based morphological compiler program is
used to develop MAG model. In the paradigm approach
a linguist or the language expert is asked to provide dif-
ferent tables of word forms covering the words in a lan- guage. Based on this information and the feature struc- ture with every word form a MAG can be build. The paradigm based approach is also well suited for highly
agglutinative language nature and this or the variant of this scheme has been used widely in NLP. Literature shows that morphological analyzers are developed for almost all Indian languages using paradigm based ap- proach.

Finite State Automata (FSA) Based Approach: Finite state machine or finite state automation FSA (or finite auto- mation) uses regular expressions and is used to accept or reject a string in a given language [9]. In general, an FSA is used to study the behavior of a system composing of state, transitions and actions. When FSA start working, it will be in the initial stage and if the automation is in any one of final state it accept its input and stops working. Two- Level Morphology Based Approach: In 1983, Kimmo Koskenniemi, a Finnish computer scientist developed a general computational model for word-form recognition and generation called Two- level morphology [9]. This development was one of the major breakthroughs in the field of morphological parsing, which is based on mor- photactics and morphophonemics concepts. The advan- tage of two- level morphology is that the model does not depend on a rule compiler, composition or any other finite-state algorithm. The "two-level" morphological approach consists of two levels called lexical and surface form and a word is represented as a direct, letter-for- letter correspondence between these forms. The Two- level morphology approach is based on the following three ideas:

Rules are symbol-to-symbol constraints that are

applied in parallel, not sequentially like rewrite rules.

The constraints can refer to the lexical context,

to the surface context, or to both contexts at the
same time.

Lexical lookup and morphological analysis are performed in tandem

Finite State Transducers (FST) Based Approach: FST is a modified version of FSA by accepting the principles of a two level morphology. A finite state transducer essen- tially is a finite state automaton that works on two (or more) tapes. The most common way to think about transducers is as a kind of ―translating machine‖ which works by reading from one tape and writing onto the other. FST‘s can be used for both analysis and generation (they are bidirectional) and it act as two level morpholo- gy. By combining the lexicon, orthographic rules and spelling variations in the FST, we can build a morpho- logical analyzer and generator at once.

Stemmer Based Approach: Stemmer uses a set of rules con-

taining list of stems and replacement rules to stripping
of affixes. It is a program oriented approach where de-
veloper has to specify all possible affixes with replace-
ment rules. Potter algorithm is one of the most widely
used stemmer algorithm and it is freely available. The advantage of stemmer algorithm is that it is very suitable to highly agglutinative languages like Dravidian lan- guages for creating MAG.

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Suffix Stripping Based Approach: Highly agglutinative lan- guages such as Dravidian languages, a MAG can be suc- cessfully build using suffix stripping approach. The ad- vantage of the Dravidian language is that no prefixes and circumfixes exist for words. Words are usually formed by adding suffixes to the root word serially. This property can be well suited for suffix stripping based MAG. Once the suffix is identified, the stem of the whole word can be obtained by removing that suffix and ap- plying proper orthographic (sandhi) rules. A set of dic- tionaries like stem dictionary, suffix dictionary and also using morphotactics and sandhi rules, a suffix stripping algorithm successfully implements MAG.

Directed Acrylic Word Graph Based Approach: Directed Acrylic Word Graph (DAWG) is a very efficient data structure that can be used for developing both morpho- logical analyzer and generator. DAWG is language in- dependent and it does not utilize any morphological rules or any other special linguistic information. But DAWG is very suitable for lexicon representation and fast string matching, with a great variety of application. Using this approach, the University of Partas Greece developed MAG for Greek language at first time. There after the method is applied for other languages includ- ing Indian languages.

2.2 MAG for Indian Languages: A Literature Sur-


Extensive work has been done already for developing MAG in various Indian languages from last ten to fifteen years. Even though there are many attempts in develop- ing MAG for Indian languages, only few works are pub- lically focused. During the literature survey, I found the following different attempts for developing MAG for Indian language NLP.

MAG for Tamil Language: Literature shows that majority of work in Indian language morphological analyser and generator was done in Tamil languages. The following are the noticeable developments in Tamil MAG. The first five MAG are the recent developments where as the re- maining MAG are developed before the year 2007.

i) AMRITA Morph Analyzer and generator for Tamil- A Rule Based Approach (2010): Dr. A.G. Menon, S. Saravanan, R. Loganathan and Dr. K. Soman, Amri- ta University, Coimbatore, developed a rule based Morphological Analyzer and generator for Tamil us- ing finite state transducer called AMAG [10]. The performance of the system is based on lexicon and orthographic rules from a two level morphological system. The system consists of list of 50000 nouns, around 3000 verbs and a relatively smaller list of ad- jectives. The proposed AMAG is compared with the existing Tamil morph analyzer and generator called ATCHARAM and proved better performance.

ii) A Novel Algorithm for Tamil Morphological Gene-

rator (2010): M.Anand Kumar, V.Dhanalakshmi and
Dr. K P Soman ,CEN, Amrita University, Coimba-
tore developed a morphological generator for Tamil based on suffix stripping algorithm [11]. The system consists of two modules, in which the first module handles the lemma/root part and the second mod- ule handles the Morpho-lexical information. The system required the following information: morpho- lexical information file, suffix table, paradigm classi- fication rules and stemming rules. Based on a sim- ple, efficient and language independent algorithm and with a less data, the system efficiently handles compound words, transitive, intransitive and also the proper nouns.

iii) An Improvised Morphological Analyzer cum Gene- rator for Tamil (2010): This work is proposed by Pa- rameswari K, CALTS, university of Hyderabad and deals with the improvised database implemented on Apertium for morphological analysis and genera- tion [12][13]. The improvised MAG uses the Finite State Transducers algorithm for one-pass analysis and generation, and the Word and Paradigm based database. The system performance is measured and compared for its speed and accuracy with the other available Tamil Morphological analyzers which were developed in CALTS and AU-KBC research Centre, Anna Unicersity. Experiment result showed that the proposed MAG performs better than the other.

iv) A Sequence Labeling Approach to Morphological Analyzer for Tamil (2010): Anand Kumar M, Dhana- lakshmi V, Soman K.P and Rajendran S of AMRITA Vishwa Vidyapeetham, Coimbatore, developed morphological analyzer for Tamil language based sequence labeling approach [14]. In the proposed work morphological analyzer problem is redefined as classification problem and solved using machine learning methodology. This is a corpus based ap- proach, where training and testing is performed with support vector machine algorithms. The train- ing corpus consists of 130,000 verb words and 70,000 noun words respectively. The system is tested with

40000 verbs and 30000 nouns taken from Amrita POS Tagged corpus. The performance of the system was also compared with other systems developed using the same corpus and results showed that SVM based approach outperform other.

v) FSA-based morphological generator for Tamil (2010): A finite state automata based morphological generator is developed by Menaka S, Vijay Sundar Ram and Sobha Lalitha Devi [15]. Two separate ex- periments were conducted for evaluate the system for nouns and verbs using both correct and wrong inputs. The experiment shows that finite-state based morphological generator is well-suited for highly agglutinative and inflectional languages like Tamil.

vi) Rajendran‘s Morphological Analyzer for Tamil: The

first step towards a preparation of morphological analyzer for Tamil was initiated by ‗Anusaraka‘

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group of researchers under the guidance of Dr Ra- jendran [16], Tamil University, Tanjavoor. ‗Anusa- raka‘ is machine translation project intended for translation between Indian languages. The devel- oped morphological analyzer for Tamil was used for Translating Tamil language into Hindi at the word level.

vii) Genesan‘s Morphological Analyzer for Tamil: Gane-

san developed a morphological analyzer for Tamil
to analyze CIIL corpus. He exploits phonological
and morphophonemic rules as well as morphotactic
constraints of Tamil in building morphological ana-
lyzer. Recently he has built an improved and effi-
cient morphological parser.

viii) Kapilan‘s Morphological Analyzer for Tamil Verbal

Forms: Another attempt was made by Kapilan and
he prepared a morphological analyzer for verbal
forms in Tamil.

ix) Deivasundaram‘s Morphological parser: Deivasun-

darm has prepared a morphological analyzer for
Tamil for his Tamil Word Processor. He too makes
use of phonological and morphophonemic rules and
morphotnatic constraints for developing his parser.

x) AUKBC Morphological Parser for Tamil: AUKBC

NLP team under the supervision of Dr Rajendran
developed a Morphological parser for Tamil. The
API Processor of AUKBC makes use of the finite
state machinery like PCKimmo. It parses, but does
not generate.

xi) Vishnavi‘s Morphological Generator for Tamil:

Vaishnavi researched for her M.Phil. dissertation on morphological generator for Tamil. The Vaishanvi‘s morphological generator implements the item and process model of linguistic description. The genera- tor works by the synthesis method of PCKimmo.

xii) Ramasamy‘s Morphological Generator for Tamil:

Ramasamy has prepared a morphological generator for Tamil for his MPhil dissertation.

xiii) Winston Cruz‘s Parsing and Generation of Tamil Verbs: Winston Cruz makes use of GSmorph me- thod for parsing Tamil verbs. GSmorph too does morphotactics by indexing. The algorithm simply looks up two files to see if the indices match or not. The processor generates as many forms as it parses and uses only two files.

xiv) Vishnavi‘s Morphological Analyzer for Tamil:

Vaishnavi again researched for her Ph.D. disserta-
tion on the preparation of Morphological Analyzer
for Tamil. She proposes a hybrid model for Tamil. It
finds its theoretical basis in a blend of IA and IP
models of morphology. It constitutes an in-built lex-
icon and involves a decomposition of words in
terms of morphemes within the model to realize
surface well-formed words-forms. The functioning can be described as defining a transformation de- pending on the morphemic nature of the word stem. The analysis involves a scanning of the string from
the right to left periphery scanning each suffix at a time stripping it, and reconstructing the rest of the word with the aid of phonological and mophopho- nemic ruels exemplified in each instance. This goes on till the string is exhausted. For the sake of com- parison she implements AMPLE and KIMMO mod- els. She also evaluates TAGTAMIL, API Analyzer, and GSMorph. She concludes that Hybrid model is more efficient than the rest of the models.

xv) Dhurai Pandi‘s Morphological Generator and Pars- ing Engine for Tamil Verb Forms: It is a full-fledged morphological generator and a parsing engine on verb patterns in modern Tamil.

xvi) RCILTS-T‘s Morphological analyzer for Tamil: Re- source Centre for Indian Language Technological Solutions-Tamil has prepared a morphological ana- lyzer for Tamil. It is named as ‗Atcharam‘. ‗Atcha- ram‘ takes a derived word as input and separate in- to root word and associated morphemes. It uses a dictionary of 20000 root words based on fifteen cat- egories. It has two modules - noun and verb analyz- er based on 125 rules. It uses heuristic rules to deal with ambiguities. It can handle verb and noun in- flections.

xvii) RCILTS-T‘s Morphological generator for Tamil: Re- source Centre for Indian Language Technological Solutions-Tamil also developed a morphological

generator for Tamil. It is named as ‗Atchayam‘.
‗Atchayam‘ generates words when Tamil morphs are given as input. It has two major modules – noun and verb generators. The noun section handles suf- fixes like plural markers, oblique form, case markers and postpositions. The verb section takes tense and PNG makers, relative and verbal participle suffixes, and auxiliary verbs. It uses sandhi rules and125 morphological rules. It handles adjectives and ad- verbs. It has word and sentence generator interfaces.

MAG for Kannada Language: There are five different de- velopments in Kannada language MAG. The first at- tempt was made by T. N. Vikram and Shalini R Urs in the year 2007 and they developed a morphological ana- lyzer prototype model based on finite state machine. Amrita University, Coimbatore, developed two separate MAG systems for Kannada language. The first Morpho- logical Analyzer developed was based on statistical ap- proach where as the other MAG was based on rule based approach. The R V College of Engineering, Banga- lore proposed another morphological analyzer and ge- nerator using Trie data structure. Using Network and Process Model, University of Hyderabad developed a MAG system for Kannada language.

i) T. N. Vikram and Shalini R Urs developed a proto- type of morphological analyzer for Kannada lan- guage (2007) based on Finite State Machine [13]. This is just a prototype and does not handle com- pound formation morphology and can handle maximum 500 distinct nouns and verbs.

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ii) A paradigm based morphological analyzer for Kan- nada language verbs using the machine learning approach was developed by Antony P J, M Anand Kumar and Dr Soman KP in the year 2010 [17]. The proposed morphological analyzer was designed us- ing sequence labeling approach and training, testing and evaluations were done by support vector me- thod (SVM) algorithms. The system captures the various non-linear relationships and morphological features of Kannada language in a better and simp- ler way. The performance of the system was eva- luated using SVMTeval tool. The system perfor- mance is considerably increased by adding more in- put words to the training corpus whose correspond- ing output are incorrect during testing and evalua- tion. From the experiment we found that the per- formance of our system significantly outperforms and achieves a very competitive accuracy of 96.25% for Kannada verbs.
iii) Ramasamy Veerappan, Antony P J, Saravanan S and Dr Soman KP developed a rule based MAG for Kannada language using finite state transducer (FST) in the year 2011 [18].The proposed MAG is ca- pable of analyzing and generating a list of twenty thousand nouns, around three thousand verbs and a relatively smaller list of adjectives. The uniqueness of the proposed MAG is its capacity to generate and analyze transitive, causative and tense forms apart from the passive constructions, auxiliaries and ver- bal nouns. This MAG was developed as part of the development of a machine translation system for English to Kannada language.
iv) Kannada Morphological Analyser and Generator Using Trie (2011): Using rule based with paradigm approach, Shambhavi. B. R and Dr. Ramakanth Ku- mar P of R V College of Engineering, Bangalore proposed a morphological analyzer and generator for Kannada language [8]. They used Trie as a data- structure for the storage of suffixes and root words. The disadvantage of Trie is that it consumes more
memory as each node can have at most ‗y‘ children, where ‗y‘ is the alphabet count of the language. As a result it can handle up to maximum 3700 root words and around 88K inflected words.
v) MORPH- A network and process model for Kan-
nada morphological analysis/ generation was de-
veloped by K. Narayana Murthy and the reported
performance of the system is 60 to 70% on general
texts [19]. The advantage of finite state network is
that, it captures all the affixes, their ordering and the
various combinations permitted in a declarative and
bidirectional fashion. Since the same network is
used both for analysis and generation, it reduces the
overall overhead of the system.

MAG for Malayalam Language: In case Malayalam, there

are two different developments in MAG as follow:
i) Malayalam Morphological Analyser and Tamil
Morphological Generator for Malayalam - Tamil Machine Translation (2011): Based on suffix strip- ping and suffix joining approach, using a bilingual dictionary, a Malayalam morphological analyzer and a Tamil morphological generator have been de- veloped by Jisha P.Jayan, Rajeev R R and Dr. S Ra- jendran [20]. The developed analyzer and generator were used for Malayalam - Tamil machine transla- tion.
ii) Morphological analyzer for Malayalam verbs (2008): Saranya S.K and Dr Soman K P of AMRITA Vishwa Vidyapeetham, Coimbatore developed a prototype morphological analyzer for Malayalam language based on hybrid approach of Paradigm and Suffix Stripping Method [9].

MAG for Hindi Language: Even though there are many attempts in MAG for Hindi language, only one devel-

opment is available publically. Teena Bajaj proposed a method for extending the range of existing morphologi- cal analyzer system for Hindi language [21]. The work focuses on how strength of existing morph analyzer can be improved by merging it with a semi-supervised ap- proach for learning of Hindi morphology.

MAG for Punjabi Language: Punjabi Morphological Ana- lyzer and Generator (year): There are two different at- tempts to develop Morphological Analyzer and Genera-

tor for Panjabi language [22]. Under ‗Anusarka‘ project, IIIT Hyderabad developed a Punjabi Morph at first time. Later Dr Mandeep Singh, Advanced Centre for Techni- cal Development of Punjabi Language, Punjabi Univer- sity developed a MAG for Panjabi language.

MAG for Bengali Language: Development of a morpho- logical analyser for Bengali (2009): An open-source mor- phological analyser for Bengali Language using finite state technology was developed by Abu Zaher Md. Faridee and Francis M. Tyers [23]. This is the first open source attempt in creating a fully-functional morpho- logical analyser and the system is currently in under development stage.

MAG for Assamese, Bengali, Bodo and Oriya Languag- es: Morphological analyzer using rule based affix strip- ping approach (2011): The design and development of morphological analyzers for four Indian languages- As- samese, Bengali, Bodo and Oriya was proposed by Mona Parakh and Rajesha N, CIIL Mysore [24]. At present it is an ongoing work based on dictionary based and suffix stripping approach and the performance of the system directly related to the size of the dictionary. The devel- oped prototype model currently can handles inflectional suffixes and work is going to handle derivation as well as prefixation.



Parsing of sentences is considered to be an important intermediate stage for semantic analysis in natural lan-

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guage processing (NLP) application such as information retrieval (IR), information extraction (IE) and question answering (QA). The study of structure of sentence is called syntax. It attempts to describe the grammatical order in a particular language in term of rules which in detail explain the underlying structure and a transforma- tional process. Syntax provides rules to put together words to form components of sentences and to put to- gether these components to form meaningful sentences. In natural language processing, syntactic parsing or more formally syntactic analysis is the process of analyz- ing and determining the structure of a text which is made up of sequence of tokens with respect to a given formal grammar. Because of the substantial ambiguity present in the human language, whose usage is to con- vey different semantics, it is much difficult to design the features for natural language processing tasks. The main challenge is the inherent complexity of linguistic phe- nomena that makes it difficult to represent the effective features for the target learning models.

3.1 Parsing Approaches

A well known parsing approach known as Nivre’s parser was successfully implemented in a variety of languages like relatively free-word order language like Turkish, inflection- ally rich language like Hindi, fixed word order language like English, and relatively case-less and less inflectional language like Swedish. Another simple approach called

‘Context Free Grammar’ (CFG) formalism was used in lan- guages like Dutch, Turkish and English to develop parsers. In order to suit the context of Indian languages, a formalism called ‘Paninian Grammar Framework’ was developed. Collin’s and Mc-Donald’s parser are the other well known parsing techniques. Generally, natural language parsing can be broadly classified in to three categories: (i) rule based (ii) statistical based and (iii) generalized parsers [25]. All the developed parsers belong to any one of these categories and follow either ‘top-down’ or ‘bottom-up’ direction. Statistic- al and rule based parsing techniques are called ‘data- driven’ and ‘grammar-driven’ approaches respectively.

Rule Based Parsers: A rule- based parser uses the hard – coded rules to identify the best parse tree for a given grammar. In a rule based parsing, production rules are ap- plied recursively and as a result overlapping problem may arise. Dynamic programming (DP) technique can be used to solve the overlapping problem efficiently. The cache for sub parse trees in the DP-based parsers is called the ‘chart’ and consequently the DP-based parsers are called ‘chart pars- ers’. The CYK algorithm developed by Cocke (1970), Kasa- mi (1965), Younger (1967) and Early algorithm developed by Jurafsky and Martin, in 2000 belong to rule based pars- ers.

Statistical Based Parsers: The main effort in parsing of a sen- tence is to resolve the ambiguities. It is very hard to write complex rules to resolve such ambiguities. In contrast to the

rule based approach, statistical parsing algorithms collect statistical data from correctly parsed sentences, and resolves ambiguity by experience. The advantage of statistical ap- proach is that it covers the whole grammar usage of the language. The performance of the statistical parsers de- pends on training corpus used to gather statistical informa- tion about the grammar of the language. Instead of using rules to find the correct parse tree, statistical parsers select the best parse tree from possible candidates based on the statistical information. Sampson proposed the first statistic- al based parsing in 1986, using a manually designed lan- guage model based on a set of transition networks, and a stimulated annealing decoding search algorithm. CFG and Probabilistic Context Free Grammar (PCFG) based parsers are the examples for statistical parsers.

Generalized parsing: The framework behind both rule based and statistical parsing are similar. Using this ad- vantage, Goodman proposed a general parsing algo- rithm based on semiring idea. In the year 2005, Melamed suggested another generalized parsing algorithm which was based on semiring parsing. Melamed generalized algorithm consists of five components such as: grammar, logic, semiring, search strategy and termination condi- tion. As the name suggests, grammar defines terminal and non-terminal symbols, as well as a set of production rules. Logic defines the mechanism of how the parser runs by generating new partial parse trees. The semiring defines how partial parse trees are scored. The search strategy defines the order in which partial parse trees are processed and the termination condition defines when to stop the logic necessarily.

3.2 Major Parser Developments in Indian

Languages: A Literature Survey

Even though natural language parsers play an important role in machine translation, it is still an ongoing process for Indian languages. Comparing with foreign languages, a very little work has been done in the area of natural lan- guage processing for Indian languages. This section will give a brief description about various developments contri- buted towards natural language parsing in Indian languag- es.

i) Antony P J, Nandini J. Warrier and Dr Soman K P

have developed a Penn Treebank based statistical syn- tactic parsers for Kannada language in 2010 [5]. The well known grammar formalism called Penn Treebank structure was used to create the corpus for proposed statistical syntactic parser. The parsing system was trained using Treebank based corpus which consisted of 1,000 Kannada sentences that was carefully con- structed. The developed corpus has been already anno- tated with correct segmentation and Part-Of-Speech (POS) information. The developers used their own

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SVM based POS tagger generator for assigning proper tags to each and every word in the training and test sentences. The proposed syntactic parser was imple- mented using supervised machine learning and proba- bilistic context free grammars (PCFG) approaches. Training, testing and evaluation were done by support vector method (SVM) algorithms. Experiment shows that the performance of the proposed system is signifi- cantly good and has very competitive accuracy.

ii) In the year 2009, B.M. Sagar, Shobha G and Rama-

kanth Kumar P proposed a way of producing context free grammar for the Noun Phrase and Verb Phrase agreement in Kannada Sentences [6]. In this approach, a recursive descent parser is used to parse the CFG. The system works in two stages: First, it generates the context free grammar of the sentence. In the second stage, a recursive descent parser called Recursive Des- cent Parser of Natural Language Tool Kit (NLTK) was used to test the grammar. As a summary, it is a gram- mar checking system such that for a given sentence parser says whether the sentence is syntactically cor- rect or wrong depending upon the Noun and Verb agreement. They have tested the system using around

200 sample sentences and obtained encouraging re- sults.

iii) Natural Language constructs for Venpa class of Tamil Poetry using Context Free Grammar was implemented by Bala Sundara Raman L, Ishwar S, and Sanjeeth Ku- mar Ravindranath in 2003 [7]. They used Push Down Automata parser to parse the CFG in the proposed sys- tem.

iv) A rule based grammar checking mechanism for Hindi

sentences was developed by Singh and D.K. Lobiyal in

1993 [8]. The system is suitable for all types of sen-

tences with compound, conjunct or complex verb phrases. In the proposed model, verb and suffixes have been divided into finer subcategories to simplify the process of associating semantics with syntax. Using Lexical Functional Grammar formalism, the base grammar rules are being augmented with functional equations. This technique is used to bridge the gap be- tween syntax and semantics of a sentence. They have developed a parser for the grammar. The grammar rules are assigned priority in a manner that the most frequently applicable rule gets higher priority than the less frequently applicable rule. The system works in such a way that, the grammar rules get fired on priori- ty basis to make the parsing efficient.

v) Selvam M, Natarajan. A M, and Thangarajan R pro-

posed a statistical parsing of Tamil sentences using phrase structure hybrid language model in the year

2008 [9]. They have built a statistical language model

based on Trigram for Tamil language with medium of

5000 words. In the experiment they showed that statis-

tical parsing gives better performance through tri-gram probabilities and large vocabulary size. In order to overcome some disadvantages like focus on semantics rather than syntax, lack of support in free ordering of words and long term relationship of the system, a structural component is to be incorporated. The devel- oped hybrid language model is based on a part of speech tagset for Tamil language with more than 500 tags. The developed phrase structured Treebank was based on 326 Tamil sentences which covers more than

5000 words. The phrase structured Treebank was trained using immediate head parsing technique. Two test cases with 120 and 40 sentences have been selected from trained set and test set respectively. They re- ported that, the performance of the system is better than the grammar model.

vi) Akshar Bharati and Rajeev Sangal described a gram-

mar formalism called the ‘Paninian Grammar Frame- work’ that has been successfully applied to all free word Indian languages [10]. They have described a constraint based parser for the framework. Paninian framework uses the notion of karaka relations between verbs and nouns in a sentence. They showed that the Paninian framework applied to modern Indian lan- guages will give an elegant account of the relation be- tween vibhakti and karaka roles and that the mapping is elegant and compact.

vii) B.M. Sagar, Shobha G and Ramakanth Kumar P pro- posed a Context Free Grammar (CFG) Analysis for simple Kannada sentences in 2010 [11]. They have ex- plained the writing of Context Free Grammar (CFG) for a simple Kannada sentence with two sets of exam- ples. In the proposed system, a grammar is parsed with Top-Down and Bottom-Up parsers and they found that a Top-Down parser is more suitable to parse the given grammatical production.

viii) A dependency parser system for Bengali language was

developed by Aniruddha Ghosh, Pinaki Bhaskar, Ami- tava Das and Sivaji Bandyopadhyay in 2009 [12]. They have performed two separate runs for Bengali. A statis- tical CRF based model followed by a rule-based post- processing technique has been used. They have used ICON 2009 datasets for training the system. They have trained the probabilistic sequence model with the mor- phological features like root word, POS-tag, chunk tag, vibhakti and dependency relation from the training set data. The output of the baseline CRF based system is filtered by a rule-based post-processing module by us- ing the output obtained through the rule based depen- dency parser. The system demonstrated an unlabeled attachment score (UAS) of 74.09%, labeled attachment score (LAS) of 53.90% and labeled accuracy score (LS) of 61.71% respectively.

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ix) Sengupta,P.and B.Chaudhuri proposed a delayed syn- tactic-encoding-based LFG parsing strategy for an In- dian Language- Bangla, in 1997 [13]. This was just an attempt in parsing of Bengali language based on the detection and formation of the proper rule set to identi- fy characteristics of inter-chunk relations. They have tested the system on a sample of about 250 simple and complex sentences picked from newspaper clippings. Results show that, even though phrasal orderings were quite random, almost all simple sentences in active voice were correctly parsed.

x) Parsing of Indian Languages using the freely available

Malt- Parser system was developed by Joakim Nivre in 2009 [14]. He developed transition-based depend- ency parsers using Malt- Parser system for three Indian languages like Bangla, Hindi and Telugu. With the Malt- Parsing technique he showed that, parsing can be performed in linear time for projective dependency trees and quadratic time for arbitrary trees. A small test set of 150 sentences was used to analyse the perform- ance of the system. The performance of the system was slightly better for Bangla and Hindi languages but for Telugu it was lower than the baseline results.

xi) Hiring world’s leading dependency parsers to plant Indian trees, a voting parser was proposed by Daniel Zeman in 2009 [15] called Maximum Spanning Malt. The system consists of three existing, freely available dependency parsers, two of which (MST and Malt) have been known to produce state-of-the-art structures on data sets for other languages. Various settings of the parsers were explored in order to adjust them for the three Indian languages like Hindi, Bengali and Telugu, and a voting approach was used to combine them into a super parser. He showed that ‘case’ and ‘vibhakti’ are important features for parsing Hindi while their usability in Bangla and Telugu is limited by data sparseness. Based on these features, he developed best combined parsers for these languages.

xii) A constraint based Dependency parsing has been at-

tempted and applied to a free-word order language

Bangla by Sankar, Arnab Dhar and Utpal Garain in

2009 [16]. They have used a structure simplification

and demand satisfaction approach to dependency pars- ing in Bangla language. A well known and very effec- tive grammar formalism for free word order language called Paninian Grammatical model was used for this purpose. The main idea behind this approach was to simplify complex and compound sentential structures first, then to parse the simple structures so obtained by satisfying the ‘Karaka’ demands of the Demand Groups (Verb Groups) and to rejoin such parsed struc- tures with appropriate links and Karaka labels. A Treebank of 1000 annotated sentences was used for training the system. The performance of the system

was evaluated with 150 sentences and achieves accura- cies of 90.32%, 79.81%, and 81.27% for unlabeled at- tachments, labelled attachments and label scores, re- spectively.

xiii) Bharat Ram Ambati, Phani Gadde and Karan Jindal

explored two data-driven parsers called Malt and MST on three Indian languages namely Hindi, Telugu and Bangla in 2009 [17]. In their experiment, they merged both the training and development data and did 5-fold cross-validation for tuning the parsers. They also ex- tracted best settings from the cross validation experi- ments and these settings are applied on the test data of the contest. Finally they evaluated the individual and average results on both coarse-grained and fine- grained tagset for all the three languages. They ob- served that for all the languages Malt performed better over MST+maxent. They also modified the implemen- tation of MST to handle vibhakti and TAM markers for labelling. They reported that, the average of best unla- beled attachment, labelled attachment and labelled ac- curacies are 88.43%, 71.71% and 73.81% respectively.

xiv) A hybrid approach for parsing Bengali sentences was

proposed by Sanjay Chatterji, Praveen Sonare, Sude- shna Sarkar and Devshri Roy in 2009 [18]. The system was based on data driven dependency parser. In order to improve the performance of the system, some hand- crafted rules are identified based on the error patterns on the output of the baseline system.

xv) A constraint based Hindi dependency parser was de- veloped by Meher Vijay Yeleti and Kalyan Deepak in

2009 [19]. In the proposed system a grammar driven approach was complemented by a controlled statistical strategy to achieve high performance and robustness. The proposed system uses two stage constraint based hybrid approach to dependency parsing. They defined two stages and this division leads to selective identifi- cation and resolution of specific dependency relations at the two stages. They also used hard constraints and soft constraints to build an efficient and robust hybrid parser. From the experiment they found out that the best labelled and unlabeled attachment accuracies for Hindi are 62.20% and 85.55% respectively.

xvi) Prashanth Mannem proposed a bidirectional depend- ency parser for Hindi, Telugu and Bangla languages in

2009 [20]. The developed parser uses a bidirectional parsing algorithm with two operations projection and non-projection to build the dependency tree. The per- formance of the proposed parser was evaluated based on the test data sentences. He reported that the system achieves a labelled attachment score of 71.63%, 59.86% and 67.74% for Hindi, Telugu and Bangla respectively on the treebank with fine-grained dependency labels. Based on the coarse-grained labels the dependency

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The research paper published by IJSER journal is about Computational Morphology and Natural Language Parsing for Indian Languages: A Literature Survey 9

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parser achieved 76.90%, 70.34% and 65.01% accuracies respectively.

xvii) Pradeep Kumar Das proposed a generative approach

to the computation of basic verbal-strings in Hindi in

2008 [21]. He described a way to examine the possibil-

ity of developing a computational parser for verb mor- phology in Hindi that would generate correct verbal stems for different kinds of tense and aspects.

xviii) A parsing criteria for Assamese text was described by Navanath Saharia, Utpal Sharma and Jugal Kalita in

2011 [22]. They described the practical analysis of As- samese sentences from a computational perspective rather than from linguistics perspective. This approach can be used to parse the simple sentences with multiple noun, adjective and adverb clauses.

xix) An attempt to study the semantic relation of Causality or Cause-Effect was proposed by Sobha Lalitha Devi and Menaka S in 2011 [23]. They also described how semantic relation of Causality is marked in Tamil, how the causal markers in Tamil manifest in texts, their syn- tactic and semantic properties and how this informa- tion can be represented so as to handle causal informa- tion and reasoning.

xx) Akshar Bharati, Mridul Gupta, Vineet Yadav, Karthik Gali and Dipti Misra Sharma proposed a simple parser for Indian Languages in a dependency framework in

2009 [24]. They described a parser which uses a

grammar driven approach to annotate dependency re- lations in both inter and intra chunk at an intermediary level. They described a grammar oriented model that makes use of linguistic features to identify relations. The proposed parser was modelled based on Paninian grammatical approach which provides a dependency grammar framework. They also compared the pro- posed parser performance against the previous similar attempts and reported its efficiency.

xxi) An approach to expedite the process of manual annota-

tion of a Hindi dependency Treebank was described by Gupta, Vineet Yadav, Samar Husain and Dipti Misra Sharma [25]. They proposed a way by which consis- tency among a set of manual annotators could be im- proved. They have also showed that their setup can useful for evaluating, when an inexperienced annotator is ready to start participating in the production of the treebank. The performance of the system was eva- luated on sample sets of data.

xxii) An unlabeled dependency parsing on graph based

method for building multilingual weakly super- vised dependency parsers for Hindi language was proposed by Jagadeesh Gorla, Anil Kumar Singh, Rajeev Sangal, Karthik Gali, Samar Husain, and Sri- ram Venkatapathy [5]. The system consists of two steps where the first step involves marking the chunks and the chunk heads of a given sentence and
then identifying the intra-chunk dependency rela- tions. The second step involves learning to identify the inter-chunk dependency relations. They re- ported that the system achieved a precision of
80.83% for sentences of size less than 10 words and
66.71% overall. They concluded that the result ob-
tained was significantly better than the baseline in
which random initialization is used.


In this paper work, we have presented a survey on dif- ferent developments of morphological analyzer and ge- nerator as well as natural language parsers for Indian languages. Additionally I tried to give a brief idea about the existing approaches that have been used to develop morphological analyzer and generator as well as natural language parsers for Indian languages. From the survey we have found that almost all approaches were applied in different morphological analyzer and generator for Indian languages. On the other hand, the literature re- veals that almost all existing Indian languages parsers are based on statistical and hybrid approach. We also noted that, the main effort and challenge behind each and every development is to design the system by con- sidering the agglutinative and morphological rich fea- tures of language.


We acknowledge our sincere gratitude to Mr. Benjamin Peter (Assistant Professor, MBA Dept, St.Joseph Engineer- ing College, Mangalore, India) and Mr. Rakesh Naik (As- sistant Professor, MBA Dept, St.Joseph Engineering Col- lege, Mangalore, India) for their valuable support regarding proof reading and correction of this survey paper.


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