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CHAPTER7

SYNTACTIC PARSERFOR KANNADA LANGUAGE

This chapter deals with the development of Penn Treebank based statistical syntactic

parsers for Kannada language. Syntactic parsing is the task of recognizing a sentence and

assigning a syntactic structure to it. A syntactic parser is an essential tool used for various

NLP applications and natural language understanding. The well known grammar

formalism called Penn Treebank structure was used to create the corpus for developed

statistical syntactic parser. The parsing system was trained using Treebank based corpus

consists of 1,000 distinct Kannada sentences that was carefully created. The developed

corpus has been already annotated with correct segmentation and POS information. The

developed system uses an SVM based POS tagger generator as explained in chapter 5, for

assigning proper tags to each and every word in the training and test sentences.

Penn Treebank corpora have proved their value both in linguistics and language

technology all over the world. At present a lot of research has been done in the field of

Treebank based probabilistic parsing successfully. The main advantage of Treebank based

probabilistic parsing is its ability to handle the extreme ambiguity produced by context-

free natural language grammars. Information obtained from the Penn Treebank corpora

has challenged the intuitive language study for various NLP purposes [168]. South

Dravidian language like Kannada is morphologically rich in which a single word may

carry different sorts of information. The different morphs composing a word may stand

for, or indicate a relation to other elements in the syntactic parse tree. Therefore, it is a

challenging task to the developers in terms of the status of the orthographic words in the

syntactic parse trees.

The proposed syntactic parser was implemented using supervised machine learning

and PCFG approaches. Training, testing and evaluation were done by SVM algorithms.

Experiment shows that the performance of the developed system is significantly well and

has very competitive accuracy.

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7.1 RELATED WORK

A series of statistical based parsers for English were developed by various researchers

namely: Charniak-1997, Collins-2003, Bod et al. - 2003 and Charniak and Johnson- 2005

[169,170]. All these parsers were trained and tested on the standard benchmark corpora

called WSJ. A probability model for a lexicalized PCFG was developed by Charniak

in1997. In the same time Collins also describes three generative parsing models, where

each model is a refinement of the previous one, and achieving improved performance. In

1999 Charniak introduced a much better parser called maximum-entropy parsing

approach. This parsing model is based on a probabilistic generative model and uses a

maximum-entropy inspired technique for conditioning and smoothing purposes. In the

same period Collins also present a statistical parser for Czech using the Prague

Dependency Treebank. The first statistical parsing model based on a Chinese Treebank

was developed in 2000 by Bikel and Chiang. A probabilistic Treebank based parser for

German was developed by Dubey and Keller in 2003 using a syntactically annotated

corpus called „Negra‟. The latest addition to the list of available Treebank is the „French

Le Monde‟corpus and it was made available for research purposes in May 2004. Ayesha

Binte Mosaddeque & Nafid Haque wrote CFG for 12 Bangla sentences that have taken

from a newspaper [76]. They used a recursive descent parser for parsing the CFG.

7.2 THEORETICAL BACKGROUND

7.2.1 Parsing

Syntactic analysis is the process of analyzing a text or sentence that is made up of a

sequence of words called tokens, and to determine its grammatical structure with respect

to given grammatical rules.Parsing is an important process in NLP, which is used to

understand the syntax and semantics of a natural language sentences confined to the

grammar. Parsing is actually related to the automatic analysis of texts according to a

grammar. Technically, it is used to refer to the practice of assigning syntactic structure to a

text.On the other way, a parser is a computational system which processes input sentence

according to the productions of the grammar, and builds one or more constituent structures

called parse trees which conform to the grammar.

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Before a syntactic parser can parse a sentence, it must be supplied with information

about each word in the sentence.In another way, a parser accepts a sequence of words and

an abstract description of possible structural relations that may hold between words or

sequences of words in some language as input and produces zero or more structural

descriptions of the input as output,as permitted by the structural rule set. There will be

zero descriptions, if either the input sequence cannot be analyzed by the grammar, i.e. is

ungrammatical, or if the parser is incomplete, i.e. fails to find all of the structure the

grammar permits. There will be more than one description if the input is ambiguous with

respect to the grammar, i.e. if the grammar permits more than one analysis of the input.

In English, countable nouns have only two inflected forms,singular and plural, and

regular verbs have only four inflected forms: the base form, the -s form, the -ed form, and

the –ing form. But the case is not same for a language like Kannada, which may have

hundreds of inflected forms for each noun or verb. Here an exhaustive lexical listing is

simply not feasible. For such languages, one must build a word parser that will use the

morphological system of the language to compute the part of speech and inflectional

categories of any word.

7.2.1.1 Top-Down Parser

Top-down parsing can be viewed as an attempt to find left-most derivations of an

input-stream by searching for parse trees using a top-down expansion of the given formal

grammar rules. Tokens are consumed from left to right. Inclusive choice is used to

accommodate ambiguity by expanding all alternative right-hand-sides of grammar rules. It

starts from the start symbol S, and goes down to reach the input. This is an advantage of

this method. The top-down strategy never wastes time for exploring trees that cannot

result in an S (root), since it begins by generating just those trees. This means it also never

explores sub trees that cannot find a place in some S-rooted tree. But this also has its

disadvantages. While it does not waste time with trees that do not lead to an S, it does

spend considerable effort on S trees that are not consistent with the input. This weakness

in top-down parsers arises from the fact that they generate trees before ever examining the

input. Recursive descent parser and LL parsers are examples of this parser.

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7.2.1.2 Bottom-up Parser

A parser can start with the input and attempt to rewrite it to the start symbol.

Intuitively, the parser attempts to locate the most basic elements, then the elements

containing these, and so on. LR parsers are examples of bottom-up parsers. Another term

used for this type of parser is Shift-Reduce parsing. The advantage of this method is, it

never suggests trees that are not at least locally grounded in the input. The major

disadvantage of this method is that, trees that have no hope of leading to an S, or fitting in

with any of their neighbors, are generated with wild abandon. LR parser and Operator

Precedence parsers are examples of this type of parsers.

Another important distinction is whether the parser generates a leftmost derivation or

a rightmost derivation. LL parsers will generate a leftmost derivation and LR parsers will

generate a rightmost derivation (although usually in reverse).

7.2.2 Syntactic Tree Structure

The different parts-of-speech tags and phrases associated with a sentence can be easily

illustrated with the help of a syntactic structure. Fig. 7.1 below shows the output syntactic

tree structure produced by a syntactic parser for the Kannada input sentence

„ರಭಚೆಂಡನನುಎಸೆದನನ‟ (Rama threw the ball).

Fig. 7.1: Syntactic tree structure

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For a given sentence, the corresponding syntactic tree structure conveys the following

information.

7.2.2.1 Part-of-Speech-Tags

The syntactic trees help in identifying the tags of all the words in a given sentence as

shown in Table 7.1.

Table 7.1: Parts-of-Speeches and Phases in a Sentence

Parts-of-speech Phrases

Node (Word) Tag Phrase Name of Phrase

ರಭ NNP ರಭ Noun phrase

ಚೆಂಡನನು NN ಚೆಂಡನನುಎಸೆದನನ Verb phrase

ಎಸೆದನನ VF

7.2.2.2Identification of Phrases

The syntactic tree also helps in identifying the various phrases and the organization of

these phrases into a sentence. In the above example there are two phrases as shown in

Table 7.1.

7.2.2.3 Useful Relationship

The syntactic tree structure also helps in identifying the relationships between

different phrases or words. Indirectly it identifies the relationship between different

functional parts like subject, object, and verb in a sentence. In the given example, the

subject part is ರಭ, object is ಚೆಂಡನ and the verb is ಎಸೆದನನ. Given a rule S ->NP VPin the

above example, the NP is the subject of the verb within the Verb Phrase (VP). In this

particular example, „ರಭ„(rAma) is the subject of „ಎಸೆದನನ„(esedanu). Similarly the rule

VP ->NP VPindicates an object-verb relationship. In our example „ಚೆಂಡನ‟ (ceMDu) is the

object of verb „ಎಸೆದನನ „(esedanu).

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7.2.3 Context Free Grammars

CFG, sometimes called a phrase structure grammar plays a central role in the

description of natural languages. In general a CFG [172]) is a set of recursive rewriting

rules called productions that are used to generate patterns of strings and it consists of the

following components:

A finite set of terminalsymbols (∑).

A finite set of non-terminalsymbols (NT).

A finite set of productions (P).

A start symbol (S).

Consider an example for simple declarative sentence ರಭಚೆಂಡನನುಎಸೆದನನ (rAmu

ceMDannu esedanu). The components and the derivation of this sentence using CFG are

shown in Table 7.2. The tag assigned to each of the word is based on the Amrita POS

tagsetas inTable 5.1.

Table 7.2: CFG Generated for a Sentence

Production Rules (P) Derivation of Sentence

Derivation Rule used

S →NP VP

NP →NNP

VP→NP VP

NP→ NN

VP→VF

NNP→ ರಭ

NN→ ಚೆಂಡನನು

VF→ ಎಸೆದನನ

S →NP VP

S →NNP VP

S →ರಭ VP

S →ರಭ NP VP

S →ರಭ NN VP

S →ರಭಚೆಂಡನನು VP

S →ರಭಚೆಂಡನನು VF

S →ರಭಚೆಂಡನನುಎಸೆದನನ

S →NP VP

NP →NNP

NNP→ ರಭ

VP→NP VP

NP→ NN

NN→ ಚೆಂಡನನು

VP→VF

VF→ ಎಸೆದನನ

Where, Start Symbol: S,

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Terminal Symbols (∑): {ರಭ, ಚೆಂಡನನು, ಎಸೆದನನ},

Non-terminal Symbols (NT): {S, NP, VP, NNP, NN, VF}

7.2.4 Probabilistic Context Free Grammars

The problem of CFG is that it misses the probabilistic model which is needed in order

to disambiguate between parses. A PCFG is a probabilistic version of a CFG where each

production has a probability [174]. Probabilities of all productions rewriting a given non-

terminal must add to 1, defining a distribution for each non-terminal. The simplest way to

gather statistical information about a CFG is to count the number of times each production

rule is used in a corpus containing parsed sentences. This count is used in order to estimate

the probability of each rule being used. In our case, we estimate the rule‟s probabilities

using the relative frequency of the rule in the training set. For a generic rule “A → B C”,

this means that every time we find the symbol A, it can be substituted with the symbol B

and C. Its conditional probability is defined as in equation 7.1:

𝑃 𝐴 → 𝐵𝐶 | 𝐴 =𝑓𝑟𝑒𝑞 𝐴→𝐵𝐶

𝑓𝑟𝑒𝑞 𝐴 (7.1)

Once we have the probability of the production rules in a PCFG, the probability of a

parse tree for a particular sentence can easily be calculated by multiplying the probabilities

of the rules that has built its sub-trees. The advantage of PCFG based syntactic parser

model is that, for any two or more different sentences that have same POS tag sequence,

but have different syntactic tree structure, then the sentence structure that has more

probability would be considered or correctly parsed.

Table 7.3 shows the total probability derivation of our previous sentence

ರಭಚೆಂಡನನುಎಸೆದನನ (rAmu ceMDannu esedanu) ‟Rama threw the ball‟. Total probability

for derivation of sentence is calculated by multiplying the probabilities used to derive the

sentence.

Total probability = 1.0 * 0.05 * 0.030 * 0.015 * 0.025 * 0.010 * 0.015 * 0.015

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Table 7.3: PCFG Generated for a Sentence

Derivation of Sentence Probability of rule

used Derivation Rule used

S →NP VP

S →NNP VP

S →ರಭ VP

S →ರಭ NP VP

S →ರಭ NN VP

S →ರಭಚೆಂಡನನು VP

S →ರಭಚೆಂಡನನು VF

S →ರಭಚೆಂಡನನುಎಸೆದನನ

S →NP VP

NP →NNP

NNP→ ರಭ

VP→NP VP

NP→ NN

NN→ ಚೆಂಡನನು

VP→VF

VF→ ಎಸೆದನನ

1.000

0.050

0.030

0.015

0.025

0.010

0.015

0.015

7.2.5 Inside-Outside Algorithms (IOA)

Similar to HMM‟s forward and backward algorithm, probability of nodes in a PCFG

parse forest as the product of the inside and outside probabilities (IO probability) for the

node „Ni’[175]. This can be easily understood by considering an example, for the grammar

rule „NP → DET NN’ over the input „the man’. The corresponding node‟s IO probability

is equal to the probability of all derivations which include the „NP →DET NN’ category

over this subset of the input. For production i→ jk, the probability of the rule is

determined using the equation 7.2:

𝑃 𝑖 → 𝑖𝑗 =𝑓𝑟𝑒𝑞 𝑖→𝑖𝑗

𝑓𝑟𝑒𝑞 𝑖→𝑗𝑘 𝑗 ,𝑘 (7.2)

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Consider a CFG grammar G as a tuple {NT, Σ, P, R}, where NTand Σ elements

represent the set of non-terminal and terminal symbols of the grammar respectively. The

element P represents the set of production rules, while R represents the non-terminal

category that is considered the top grammar category. For given input sequence of

terminals of the grammar {a1,... aT}, we denote e(s, t, Ni) and f(s, t, Ni) are the inside and

outside probabilities respectively for a node „Ni’, that spans input items as to at

inclusively. Fig. 7.2 illustrates the corresponding nodes in the parse forest used when

calculating the inside and outside probabilities for Ni. Nonleaf nodes in the figure

represent NT categories, and „Nr’is the root node whose category r is in the set R. Leaf

nodes represent S categories of the grammar, that is, the input sequence {a1, ... aT}.

Fig. 7.2:The inside (e) and outside (f) regions for node Ni

7.2.5.1 Inside Probability

The inside probability e(s, t, Ni) represents the probability of sub-analyses that are

rooted with mother category „i'for this sentence over the word span s to t. Each production

is of the form i → jkwhere each set of daughter nodes „Nj‟ and „Nk‟ span from as to „ar‟

and „ar‟+1 to „at‟, respectively.Fig. 7.3 illustrates this structure for node „Ni’.Inside

probability of each node corresponds to the product of all CFG rules that are applied to

create the sub-analysis as shown in equation 7.3.

𝑒 𝑠, 𝑡, 𝑁𝑖 = 𝑃 𝑖 → 𝑗𝑘 𝑒 𝑠, 𝑟, 𝑁𝑗 𝑒𝑡−1𝑟=𝑠 𝑟 + 1, 𝑡,𝑁𝑘 𝑗 ,𝑘 (7.3)

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Fig. 7.3: Inside probabilities for node Ni

7.2.5.2Outside Probability

On the other hand, the outside probability f(s, t, Ni), for a node „Ni‟ is calculated using

all the nodes for which the node is a daughter (sub-analysis). This calculation includes the

inside probability of the other daughter nodes of which „Ni’is a member. This means,

category „i'could appear in two different settings: j→ik or j→ki, as shown in Fig. 7.4.

Fig. 7.4: Outside probabilities for node Ni

The outside probability of „Ni’is calculatedusing the outside probability of the mother

node (Nj) multiplied by the product of inside probabilities of the daughters other than

„Ni’i.e. „Nk’. In each instance when „Ni’is a daughter of a node, the outside probability f (s,

t, Ni) for a given sentence is calculated using the equation 4.

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𝑓 𝑠, 𝑡, 𝑁𝑖 = 𝑓 𝑠, 𝑟, 𝑁𝑗 𝑃 𝑗 → 𝑖𝑘 𝑒 𝑡 + 1, 𝑟, 𝑁𝑘

𝑇

𝑟=𝑡+1

𝑗 ,𝑘

+ 𝑓 𝑟, 𝑡,𝑁𝑗 𝑃 𝑗 → 𝑘𝑖 𝑒 𝑟, 𝑠 − 1,𝑁𝑘

𝑠−1

𝑟=1

𝑗 ,𝑘

(7.4)

7.2.6 Support Vector Machine as a Classifier

SVM is a useful technique for data classification. A typical use of SVM involves two

steps: first, training a data set to obtain a model and second, using the model to predict

information of a testing data set [11,176]. For Each instance in the training set contains

one “target value" (i.e. the class labels) and several “attributes" (i.e. the features or

observed variables). The goal of SVM is to produce a model (based on the training data)

which predicts the target values of the test data given only the test data attributes. The

SVMs rely on maximum-margin hyper plane classifier which is used to predict the next

action at the parsing time.

7.3 PROPOSED SYNTACTIC PARSER

7.3.1 Contribution

The most important work in thedeveloped systemwasthe creation of training data set.

The training data set plays a key role in determining the efficiency of the syntactic parser.

The more accurate the training data, the more accurate is the syntactic parser. Around

1000 diverse sentences have been taken from various Kannada grammar books and tagged

using the SVM based POS tagger. All the sentences were then manually converted into

Penn Treebank format.

7.3.2Bracketing Guidelines for Kannada Penn Treebank Corpus

Penn Treebank corpora have proved their value both in linguistics and language

technology all over the world. Information obtained from the Penn Treebank corpora has

challenged the intuitive language study for various NLP purposes [171, 173]. The main

effort for developing a Penn Treebank based corpus is to represent the text in the form of a

„Treebank‟, where tree structures represent syntactic structures of phrases and sentences.

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This is followed by an application of a parsing model to the resulting Treebank. Therefore

with the availability of Treebank of annotated sentences it is easy to develop natural

language syntactic parser and other NLP application tools. The main effort is to create

well balanced Treebank based corpus with almost all possible inflections. The developed

corpus mainly consists of simple sentences as well as some of the compound

sentences.Some of which are illustrated as follows:

7.3.2.1 Simple Declarative Sentence

Consider a simple declarative sentence ರಭಚೆಂಡನನುಎಸೆದನನ (rAma ceMDannu esedanu

„Rama threw the ball’). The Fig. 7.5 shows an example for the „Penn tree syntax‟ and the

Fig. 7.6show the corresponding parse tree for this sentence.

(S (NP (NNP ರಭ ) (VP (NN ಚೆಂಡನನು) (VF ಎಸೆದನನ ))) (. .))

Fig. 7.5:Penn Treebank format of a Declarative sentence

Fig. 7.6:Parse tree for the Fig. 7.5

7.3.2.2Imperative Sentences

Imperatives are formed from the root of the verb and usually given a null subject-SBJ,

as shown in Fig. 7.7 and7.8.

(S (NP (NNP SBJ) (VP (NN ಚೆಂಡನನು) (VF ಎಸೆದನನ ))) (! !))

Fig. 7.7:Penn Treebank format of an Imperative sentence

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Fig. 7.8:Parse tree for the Fig. 7.7

Unlike Malayalam, depends on the type of noun case that is associated with the SBJ,

the PNG markers associated with the Kannada verb also changes as shown in below.

(S (NP (PRP SBJ) (VP (NN ಚೆಂಡನನು) (VF ಎಸೆದಯನ))) (! !))

Fig. 7.9: Penn Treebank format of an Imperative sentence

Fig. 7.10: Parse tree for the Fig. 7.9

7.3.2.3 Passive Sentence

Consider a passive sentence ರಭನೆಂದಚೆಂಡನಎಸೆಮಲ್ಪಟ್ಟುತನತ (ramaniMda ceMDu

eseyalpaTTittu). The Fig. 7.11indicates the Penn Treebank format and Fig. 7.12show the

corresponding parse tree for this sentence.

(S (NP (NNP ರಭನೆಂದ) (VP (NN ಚೆಂಡನ) (VF ಎಸೆಮಲ್ಪಟ್ಟುತನತ))) (. .))

Fig. 7.11: Penn Treebank format of a passive sentence.

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Fig. 7.12: Parse tree for the Fig. 7.11

7.3.2.4 Question Sentence

Consider a questionsentence ನೋನನಏನನಮಡಿದ ?(nInu Enu mADidi?). The Fig.

7.13indicates the Penn Treebank format and Fig. 7.14 show the corresponding parse tree

for this sentence.

(S (NP (NN ನೋನನ) (VP (QW ಏನನ) (VF ಮಡಿದ ))) (? ?))

Fig. 7.13: Penn Treebank format of a question sentence

Fig. 7.14: Parse tree for the Fig. 7.13

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Questions may consists of a null subject-SBJ. Fig. 7.15 and 7.16 below shows an

example for the question with null subject.Depending on the type of noun case associated

with the SBJ, the PNG markers associated with the Kannada verb also changes

(S (NP (NNP SBJ) (VP (QW ಏನನ) (VF ಮಡಿದ ))) (? ?))

Fig. 7.15: Penn Treebank format of a question sentence

Fig. 7.16: Parse tree for the Fig. 7.15

7.3.2.5Compound Sentences

The relationship of conjoining in Kannada may be any one of: (i) additive indicated by

„ಭತನತ‟(mattu) or „ಊ‟(U) (ii) alternative indicated by „ಅಥವ‟ (adhava) or

„ಇಲ್ಿಲ್ಲಲ್ಿಳ‟(illalillave) and (iii) adversative indicated by „ಆದರ‟(Adare). Coordination

may be take place either at phrase or clause level. Fig 7.17 and 7.19 illustratestwo

examples of Treebank formatforcompound sentences coordinated at phrase level. Fig 7.18

and 7.20 shows their corresponding parse tree structures.

(S (NP (NN ಹನಡನಗಿಮಯನ) (NP (CNJ ಭತನತ) (NN ಹನಡನಗಯನ)))

(VP (NNಚೆಂಡನನು)(VP (ADV ಎಸೆದನ) (VF ಹಿಡಿಮನತ್ತತದ್ಯನ))) (. .))

Fig. 7.17: Penn Treebank format of a compound sentence coordinated at phrase level

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Fig. 7.18: Parse tree for the Fig. 7.17

(S (NP (NN ಹನಡನಗಿಮಯ ) (NN ಹನಡನಗಯ )) (VP (NN ಚೆಂಡನನು) (VP (ADV ಎಸೆದನ) (VF

ಹಿಡಿಮನತ್ತತದ್ಯನ))) (. .))

Fig. 7.19:Penn Treebank format of a compound sentence coordinated at phrase level

Fig. 7.20: Parse tree for the Fig. 7.19

The following examples illustrates the Penn Treebank formatsand corresponding

Parse trees of compound sentences coordinated at clause level

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(S (NP (NNP ರಜನ ) (VP (NN ಚೆಂಡನನು) (VF ಎಸೆದನನ ))) (VP (CNJ ಭತನತ) ( VP(NP

(NNP ರಭ )) (VP (NN ಇದನನು) (VF ಹಿಡಿದನನ ))) (..))

Fig. 7.21: Penn Treebank format of a compound sentence coordinated at clause level

Fig. 7.22: Parse tree for the Fig. 7.21

(S (NP (NP (PRN ನನು) (PPOಹತ್ತತಯ)) (VP (NN ದಯ) (VF ಇದೆ )))

(VP (CNJ ಆದರ) (VP (NN ಸ ಜಿ) (VF ಇಲ್ )ಿ)) (. .))

Fig. 7.23: Penn Treebank format of a compound sentence coordinated at clause level

Fig. 7.24: Parse tree for the Fig. 7.23

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7.3.3Architecture of Proposed Syntactic Parser

The architecture of the proposed syntactic parser is shown in Fig. 7.25. The proposed

syntactic parser model consists of the following steps:

1. Creating training set of sentences.

2. POS Tagging the training sentences.

3. Format the syntactic structure of the training sentences.

4. Training the system using svm_cfg_learn module of SVM.

5. Testing the system with parser model created in the previous step using

svm_cfg_classify module of SVM.

6. Display the output of input test sentence in syntactic tree form using Tree Viewer.

Fig. 7.25:Architecture of Proposed Syntactic Parsing System

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In any statistical system, the corpus creation is a major task which consumes

considerable time. The parser model is created with the data set containing simple

sentences and some complex sentences. The training data covers almost all the patterns

available for the simple sentences. The first three steps in the developed system were used

to create the Treebank based corpus. A brief description of each of these steps is as

follows:

7.3.3.1 Creating training set of sentences

The developed Kannada Treebank corpora consist of 1,000 random diverse Kannada

sentences. These sentences were carefully constructed by taking care of various factors for

generating good corpora.

7.3.3.2 POS Tagging the training sentences

The next step was to assign parts-of speech tags to each and every word in the

sentences using the POS tagger model. Parts-of speech tagging is an important stage in our

Treebank based syntactic parsing approach. The developed system uses a statistical based

POS tagger developed using SVMTool, for assigning proper tags to each and every word

in the training and testing sentences. More detailed information on the POS tagset and

guidelines concerning its uses are found in [177].

7.3.3.3 Format the syntactic structure of the training sentences

The major work in the corpus creation stage is to find out the syntactic structure of

each and every sentence.The developed statistical corpus was based on well known Penn

Treebank corpora, so that the syntactic format of each and every training sentence were

manually created by resolving various ambiguities and dependencies. The sentences in the

training corpus were divided into various phrases and phrases are further divided into one

or more words.

7.3.3.4 Training the system using svm_cfg_learn

SVMcfg is a flexible and extensible tool for learning models in a wide range of

domains. SVMcfg is an implementation of the SVM algorithm for learning a weighted

context free grammar. The weight of an instantiated rule can depend on the complete

terminal sequence, the span of the rule, and the spans of the children trees. Another

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important property of the SVMcfg is that, it‟s easy to add attributes that reflect the

properties of the particular domain at hand.

The SVMcfg mainly consists of two modules called learning module namely

svm_cfg_learn and classification module namely svm_cfg_classify. These modules are

used respectively for learning and classification for a set of data.SVMcfg uses the learning

module called svm_cfg_learn for learning the training corpus. The usage of this module is

much like the svm_light module and the syntax is as follows:

svm_cfg_learn -c 1.0 train.data model

Which train SVM on training set train.data and output the learned grammar to the two

model files called model.svm and model.grammar by setting the regularization parameterC

to 1.0. In the developed systems, 171 different rules were extracted from a training data of

1000 sentences as shown in table 7.4. Since the svm_cfg_leran module utilized the

probabilistic context free grammar formalism, the module also finds out the probabilities

of each and every rule as explained in section 8.

Table 7.4: Generated Rules from the Training Corpus

NP --> VNAJ NN

VP --> ADV CVB

VP_VP --> VP VP

'ROOT' --> S

NP --> PRP VP

NP --> PRP VINT

VP --> PRP VP

NP --> PRP NNQ_NN

VP --> NP

NP --> NNQ

NP --> NN

NP --> COMPINT NN

NP --> NN NNP

NP --> NNP VINT

VP --> NNP VP

NP --> NNP NNQ_NN

NP --> NN NNP_NN

VP --> VP NNP

VP --> VNAV VBG

VP --> VF

VP --> VF NNP

VP --> ADV VINT

ADJ_NNP --> ADJ NNP

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NP --> PRP

NP --> PRP NNP

NN --> PRP

NP --> NNP

NP --> NNP NNP

NP --> NNP CNJ

NP --> NN NN_ADJ

VP --> NNP CVB_VF

NN_ADJ --> NN ADJ

NNP_NNP --> NNP NNP

VP --> VNAV CVB

VP --> ADV CVB_VF

VP --> VAX

VP --> VBG

NP --> PRP ADJ_NN

NP --> DET ADJ_NN

VP --> NP VP_VP

NP --> QW

NP --> NNPC

NP --> NNP ADJ_NN

NP --> CRD ADJ_NN

NP --> CVB NNP

VP --> CVB

VP --> NP VF

S --> NP VP_.

NP --> ORD

ADV_NN_VAX --> ADV NN_VAX

VP --> NNQ VF

NP --> NN VF

VP --> NN VF

VP --> NN NN_VF

NP_VP_. --> NP VP_.

VP --> ADJ NN_VF

NNP_NNP_NNP --> NNP NNP_NNP

NN_VF --> NN VF

INT_ADJ_NN --> INT ADJ_NN

NP --> PRP VF

VP --> PRP VF

ADJ_NN_VF --> ADJ NN_VF

VP --> DET VF

VP --> DET NN_VF

PRP_VF --> PRP VF

NP --> NNP VF

VP --> NNP VF

VP --> CRD VF

VP --> COM VF

VP --> COM NN_VF

VP --> ADV VF

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NNP_VF --> NNP VF

VP --> ADV NN_VF

VP --> VAX VF

ADV_VF --> ADV VF

VP --> VBG VF

NP --> PRP ORD

S -->NP .

VP --> VINT VF

NP --> NNC NNC

S --> NP NP_VP_.

NP --> NNP COM

NP --> NNP ORD

NP --> NNP NNP_NNP_NNP

NP --> NNP COM_NN

S -->VP .

NP_. -->NP .

VP --> INT VF

VP --> INT ADV_NN_VAX

NNP_NNP_NNP_NNP --> NNP NNP_NNP_NNP

VP_. -->VP .

VP --> CVB VF

VP --> CVB NN_VF

NP --> NP NP

NP --> DET NN_NN

CVB_VF --> CVB VF

VP --> ORD VF

CVB_NN_VF --> CVB NN_VF

NP --> NN NP

NP --> CRD NN_PPO

VP --> RDW NN_VF

NP --> ADJ NP

VP --> VF ADV

NP --> PRP NP

NP --> NNP NP

NP --> NN PPO

NP --> NNP NNP_NNP_NNP_NNP

VP --> NN ADV_VF

NP --> CRD NP

VP --> VP PRP

VP --> CRD ADJ_NN_VF

NN_PPO --> NN PPO

NP --> PRP PRP

NP --> NNP PRP

VP --> NN VAX

NN_VAX --> NN VAX

VP --> ADV PRP_VAX

VP --> PRP VAX

PRP_VAX --> PRP VAX

241

VP --> NNP VAX

NP --> CRD INT_ADJ_NN

VP --> COM PRP_VF

VP --> ADV VAX

VP --> ADV PRP_VF

NP --> CVB PRP

NP --> NNQ NN

NP --> NN NN

VP --> NN VBG

NP --> NN NN_CVB

NNQ_NN --> NNQ NN

NP --> CRD ADJ_NNP

NP --> ADJ NN

NN_NN --> NN NN

VP --> ADJ NN

VP --> ADV CVB_NN_VF

ADJ_NN --> ADJ NN

NP --> PRP NN

NP --> PRP VBG

NP --> DET NN

NP --> DET VBG

NP --> NP NP_VP

S --> NP NP_.

VP --> NP NP_VP

NP --> NNP NN

NP --> NNP VBG

NP --> NN CVB

VP --> NN CVB

PRP_NN --> PRP NN

NP --> CRD NN

S --> VP NP_.

NNP_NN --> NNP NN

VP --> COM NNP_VF

NN_EMP --> NN EMP

NN_CVB --> NN CVB

NP --> ADV NN_CVB

VP --> ADV NNP_VF

NP --> VBG NN

NP --> PRP CVB

VP --> VBG NN

COM_NN --> COM NN

NP --> NP VP

VP --> NP VP

NP --> NN VP

NP --> NNP CVB

VP --> NN VP

NP_VP --> NP VP

VP --> NN VINT

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NP --> NN PRP_NN

NP --> INT NN

VP --> VP VP

NP --> ADJ NN_EMP

7.3.3.5 Testing the system using svm_cfg_classify

The trained model created in the previous step can be used to predict the syntactic tree

structure of new test sentences. The test file containing the test sentences is given to the

POS tagger model for assigning syntactic tags to each and every word in the sentence. The

result of the POS tagger is given to the svm_cfg_classify. Svm_ cfg_classify analyzes the

syntactic structure of test sentences by referring the model files that were created by

svm_cfg_learn. Svm_cfg_classify module makes predictions about the syntactic structure

of test sentences based on probabilistic context free grammar formalism and inside-outside

algorithms. The syntax of svm_cfg_classify is as follows:

svm_cfg_classify test.data model predictions

For all test examples in test.data, the predicted parse trees are written to a file called

predictions.

7.3.3.6 Display the output using Tree Viewer

NLP or Linguistic researchers who work in syntax often want to visualize parse trees or

create linguistic trees for analyzing the structure of a language. The syntactic parse tree of

the test sentence is created and displayed by using „Syntax Tree Viewer‟ software

developed using Java language.

7.4 SYSTEM PERFORMANCE AND RESULT

Even though the proposed syntactic parser system was developed with a small sized

corpus consists only 1000 distinct sentences, the result obtained was well promising and

encouraging. The performance of the system was evaluated using svm_cfg_classify

module and the incorrect outputs were noticed.On contrast to the rule based approach, the

systems performance was considerably increased by adding the input sentences to the

training corpus whose corresponding outputs were incorrect during testing and evaluation.

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Performances of the systems were evaluated with a set of 100 distinct sentences that were

not present in the corpus. Even though this is just a prototype system with very small sized

corpus, the result obtained was promising and encouraging. Fig. 7.26 shows the output

screen shot for a test sentence „ರ್ನನಒೆಂದನತರಫರಮನತತಇದೆ್ೋನೆ‟ (nAnu oMdu patra

bareyutta iddEne-I am writing a letter).

Fig. 7.26: Output Screenshot

7.5 SUMMARY

This work is used to create a prototype version of a statistical based syntactic parser

for Kannada language implemented using SVM. The training corpus consists of Penn

Treebank form of the Kannada sentences. The corpus creation is the main objective of the

work which consumes considerable time. The SVM based POS tagger is used to tag each

and every word in the training. The performance of the developed syntactic parser model

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can be improved by incorporating more syntactical information by increasing more and

more sentence types and well-formed large corpus. In future we can also use these

syntactic parsers for tree to tree translation. This will be very useful for bilingual MT from

English to South Dravidian languages. To the best of my knowledge this is the first

attempt of computationally constructing statistical based syntactic parser models for

Kannada language.

7.6 PUBLICATION

Antony P J, Nandini. J. Warrier and Soman K P: “Penn Treebank-Based Syntactic

Parsers for South Dravidian Languages using a Machine Learning Approach”,

International journal on Computer Application (IJCA), No. 08, 2010, ISBN: 978-93-

80746-92-0, Published by: Foundation of Computer Science,Abstracted and indexed in

DOAJ, Google Scholar, Informatics, ProQuest CSA Technology Research Database.

Impact factor: 0.87.