A COMPUTATIONAL GRAMMAR OF SINHALA FOR

ENGLISH-SINHALA MACHINE TRANSLATION

B. Hettige

(08/8021)

Degree of Master of Philosophy

Department of Information Technology

University of Moratuwa

Sri Lanka

December 2010

A COMPUTATIONAL GRAMMAR OF SINHALA FOR

ENGLISH-SINHALA MACHINE TRANSLATION

Budditha Hettige

(08/8021)

Thesis submitted in partial fulfillment of the requirements for the degree

Master of Philosophy

Department of Information Technology

University of Moratuwa

Sri Lanka

December 2010

Declaration of the Candidate and the Supervisor

I declare that this is my own work and this thesis does not incorporate any material

previously submitted for a Degree or Diploma in any other University or institute of

higher learning, without acknowledgement. It does not contain any material

previously published or written by another person except where the

acknowledgement is made in the text to the best of my knowledge and belief.

Also, I hereby grant to University of Moratuwa the non-exclusive right to reproduce

and distribute my thesis, in whole or in part in print, electronic or other medium. I

retain the right to use this content in whole or part in future works (such as articles or

books).

Signed

..

Budditha Hettige Date

Candidate

The above candidate has carried out research for the M. Phil. dissertation under my

supervision.

.. ..

Prof. Asoka S. Karunananda Date

. ..

Dr.

i

Abstract

Communication is fundamental to the evolution and development of all kinds of living beings. With no disputes, languages should be recognized as the most amazing artifacts ever developed by mankind to enable communication. Computer has also become such a unique machine, due to its capacity to communicate with humans through languages. It is worth mentioning that the languages understood by computers and humans are quite different, yet people can communicate with computers. This has been possible since the computer is fundamentally an artifact that can translate one language to another. Therefore, computers must be able to do language translations than any other computing task. Nowadays, computing is evolving to enable machine-machine communication with no or little human intervention, yet humans continue to face with what is called language barrier for communication. In particular, a vast collection of world knowledge written in English has been inaccessible to communities who cannot communicate in English. Such communities are unable to contribute to the development of world knowledge due to the language barrier. As a result many people have embarked into research in computer aided natural language translation. This area is commonly known as Machine Translation. Among others, Aptium, Bable fish, Google translator, SYSTRAN, EDR, Anusaaraka, AngalaHindi, AnagalaBarathi, and Mantra are some examples for popular machine translation systems. These systems use various approaches including Human-assisted, Rule-based, Corpus-based, Knowledge-based, Hybrid and Agent-based to translate from one language to another. However, due to inherent diversifications of natural languages, a generic machine translation approach is far from reality. This thesis presents a computational grammar for Sinhala language to develop English to Sinhala machine translation system with an underlying theoretical basis. This system is known as BEES, an acronym for Bilingual Expert for English to Sinhala machine translation. The concept of Varanegeema (conjugation) in Sinhala language has been considered as the philosophical basis of this approach to the development of BEES. The Varanegeema in Sinhala language is able to handle large number of language primitives associated with nouns and verbs. For instance, Varanegeema handles the language primitives such as person, gender, tense, number, preposition and subjectivity/objectivity. More importantly, Varanegeema allows deriving all associated word forms from a given base word. This enables to drastically reduce the size of the Sinhala dictionary. Since the concept of Varanegeema can be expressed by a set of rules, it nicely goes with rule-based implementation of machine translation systems. BEES implements 85 grammar rules for Sinhala nouns and 18 rules for Sinhala verbs. BEES compresses with seven modules namely English Morphological analyzer, English Parser, English to Sinhala base word translator, Sinhala Morphological Generator, Sinhala Parser, Transliteration module and Intermediate Editor. In addition to the main modules, system comprises of four dictionaries, namely, English dictionary, Sinhala dictionary, English-Sinhala Bilingual dictionary and the Concept dictionary. BEES primarily shares the features with the Rule-based, Context-based and Human-assisted approaches to machine translation. The BEES has been implemented using Java and Swi-Prolog to run on both Linux and Windows environments. The English to Sinhala Machine Translation system, BEES has been evaluated to test the hypothesis that concepts of Varanegeema can be used to drive English to Sinhala machine translation. The English to Sinhala machine translation system has been evaluated through three steps. As the first step, all the language processing primitives such as morphological analyzers, parsers, translator and the transliteration module have been tested through the white box testing approach. In order to test each module, several online testing tools

ii

including English morphological analyzer, English parser and Sinhala word generator have been implemented. By using these online tools each module has been completely tested through a carefully created test plan. In addition, an online evaluation test bed has also been implemented to continuously capture feedback from online users. This online evaluation test bed gives facilities to make different types of sentences using a given set of words. Word Error Rate and the Sentence Error Rate were calculated by using these evaluation results. Finally the intelligibility and the accuracy tests have been conducted through the human support. In order to evaluate the intelligibility and the accuracy of the English to Sinhala machine translation system, following steps were followed. Two hundred sample sentences were collected and grouped into 20 sets (10 sentences per each set). Then each sentence was translated using the English to Sinhala Machine Translation system. Each set was given to the human translators and scored. The intelligibility and the accuracy were calculated through the above evaluation results. The experimental result shows that English morphological analyzer, English parser, English to Sinhala base word translator, Sinhala morphological generator and the Sinhala sentence generator successfully work with more than 90% accuracy. Overall result of the evaluation shows 89% accuracy with the word error rate of 7.2% and the sentence error rate of 5.4%. The BEES successfully translates English sentences with simple or complex subjects and objects. The translation system successfully handles most commonly used patterns of the tenses including active and passive voice forms.

iii

Acknowledgements

This thesis is the result of four years of devoted work whereby I have been

accompanied and supported by many people. It is a pleasant aspect that I have now

the opportunity to express my gratitude for all of them.

I am grateful to the University of Moratuwa especially to the faculty of

Information Technology for providing me the opportunity to do a research study.

The first person I would like to thank is my supervisor Prof. Asoka Karunananda

for whom a few lines are too short to make a complete account of my deep

appreciation. This study would not have been such a success without his

commonsense knowledge and perceptiveness. I owe him lots of gratitude for

showing me this way of research. Besides apart from being an excellent supervisor

Prof. Karunananda has been an understanding teacher and he has provided me

support in every aspect for the success of this research.

I am also grateful to thank Dr. Sarath Bannayake, Head, Department of Statistics

and Computer Science, University of Sri Jayawardenepura for assistance he has

given to me during the research work.

With the great pleasure and deep sense of gratitude, I acknowledge Mr. P. Dias

former head; Senior Lecturer Department of Statistics and Computer Science,

University of Sri Jayawardenepura for the great help provided me to make a method

for evaluation.

I would also like to thank Mr. Niranjan Bandara, Lecturer, Department of Sinhala

and Mass Communication, University of Sri Jayawerdenepura for his valuable

support to correct some Sinhala language issues.

I would like to give my great pleasure and deep sense of gratitude to Venerable

Kirioruwe Dhamananada thera, Venerable Kukulpane Sudassi thera and Venerable

Matttumagala Chandanada thera for their valuable support given to me to solve

Sinhala and English language problems by sharing their knowledge of Sinhala, Pali

and Sanskrit Language structures.

iv

I am deeply indebted to Mr. Duminda de Silva, Head, Department of Mathematics

and Computer Science, The Open University of Sri Lanka for the encouragement

extended to me throughout this study.

I wish to extend my sincere gratitude to Ms. G. S. Makalanda, Dr. T.G.I. Fernando

and Dr. E. A. T. A. Edirisooriya, for their great support and encouragement extended

to me throughout this study.

My deepest gratitude goes to my mother and my wife for the unconditional support

given and without their support, this would have been impossible. Again, I must give

a big thank to my wife Lakshimi for tolerating my busy schedules due to the research

work. Last but not least I thank all who supported me to make this work a success.

January 3, 2011 Budditha Hettige

v

Table of Contents

Declaration of the Candidate and the Supervisor i

Abstract ii

Acknowledgements iv

Table of Contents vi

List of Figures xi

List of Tables xii

Chapter 1 Introduction 1

1.1 Preamble 1

1.2 English to Sinhala Machine Translation 2

1.3 What are Machine Translation Systems? 2

1.4 Aim of the Research 4

1.5 Objectives of the Research 4

1.6 1.5 Scope of the Project 5

1.7 Hypothesis 5

1.8 Structure of the Thesis 5

1.9 Summary 7

Chapter 2 State of the Art of Machine Translations 8

2.1 Introduction 8

2.2 Fundamentals of the Natural Language Processing 8

2.3 Machine Translation Systems 9

2.4 Current Approaches to Machine Translation 10

2.4.1 Human-assisted Machine Translation 10

2.4.2 Rule-based Machine Translation 12

2.4.2.1 Transfer-based Machine Translation 14

2.4.2.2 Interlingua Machine Translation 15

2.4.2.3 Dictionary based Machine Translation 16

2.4.3 Statistical Machine Translation 17

2.4.4 Example-based Machine Translation 18

2.4.5 Knowledge-based Machine Translation 19

2.4.6 Hybrid Machine Translation 20

2.4.7 Agent-based Machine Translation 20

2.5 Existing English to Sinhala Machine Translation Systems 21

2.6 Concepts and Techniques for Machine Translation 22

vi

2.6.1 Morphological Analysis 23

2.6.2 Syntax Analysis 24

2.7 Problem Definition 25

2.8 Summary 26

Chapter 3 Overview of the English and Sinhala Languages 28

3.1 Introduction 28

3.2 The English Language 28

3.3 The English Language Morphology 28

3.3.1 English Noun Morphology 29

3.3.2 English Verb Morphology 30

3.3.3 English Adjective Morphology 31

3.4 Syntax of the English Language 32

3.4.1 The English Sentence Subject 33

3.4.2 The English Predicate 33

3.4.3 Verb Tense 33

3.4.4 The Complement 34

3.5 Semantics of English Language 35

3.5.1 Word Level Semantics 35

3.5.2 Sentence Level Semantics 35

3.5.3 The paragraphs Level Semantics 35

3.6 The Sinhala Language 35

3.6.1 Sinhala Alphabet 36

3.7 Sinhala Language Morphology 38

3.7.1 Sinhala Noun Morphology 38

3.7.2 Sinhala Verb Morphology 41

3.8 Syntax of the Sinhala Language 43

3.9 Semantics of the Sinhala Language 44

3.10 Comparison Between English and Sinhala 44

3.10.1 Fundamental Differences 45

3.10.2 Morphological Differences 45

3.10.3 Syntax in the two Languages 46

3.11 Language Issues 46

3.11.1 Grammatical Issues 47

3.11.2 Text Manipulation Issues 47

vii

3.12 Challenges in English to Sinhala Machine Translation 48

3.12.1 Word and Sentence Segmentation 49

3.12.2 Lexical Selection 49

3.12.3 Conjugation 49

3.12.4 Tense Detection 50

3.12.5 Article Insertion 50

3.12.6 Sentence boundaries 50

3.12.7 Word Order 50

3.13 Summary 51

Chapter 4 Novel Approach to Machine Translation 52

4.1 Introduction 52

4.2 A Theoretical-based Approach to Machine Translation 52

4.3 Computational Model of Grammar for Sinhala 53

4.3.1 Computational Model for Sinhala Morphology 53

4.3.2 Context-Free Grammar for Sinhala language 53

4.4 Hypothesis 57

4.5 Approach in a Nutshell 57

4.6 Features of BEES 57

4.7 Input for BEES 58

4.8 Output of BEES 58

4.9 Process of BEES 58

4.10 Summary 59

Chapter 5 Design of BEES 60

5.1 Introduction 60

5.2 Design of BEES 60

5.2.1 English Morphological Analyzer 60

5.2.2 English Parser 62

5.2.3 English to Sinhala Base Word Translator 62

5.2.4 Sinhala Morphological Generator 63

5.2.5 Sinhala Parser 63

5.2.6 Transliteration module 64

5.2.7 Intermediate Editor 64

5.2.8 Lexical Resources 65

5.3 Supporting modules 66

viii

5.3.1 Dictionary Updater 66

5.3.2 Sinhala Word Generator 67

5.3.3 Online Search module 67

5.4 Summary 68

Chapter 6 Implementation 69

6.1 Introduction 69

6.2 Development Stages 69

6.3 Implementation of the BEES 70

6.3.1 English Morphological Analyzer 70

6.3.2 English Parser 74

6.3.3 English to Sinhala Bilingual Translator 77

6.3.4 Sinhala Morphological Generator 78

6.3.5 Sinhala Sentence Composer 81

6.3.6 Transliteration Module 82

6.3.7 Intermediate Editor 83

6.3.8 Lexical Resources 84

6.3.8.1 English Dictionary 84

6.3.8.2 Sinhala dictionary 86

6.3.8.3 English-Sinhala Bilingual dictionary 89

6.3.8.4 Concept Dictionary 90

6.4 Supporting modules 91

6.4.1 Online Updater 91

6.4.2 Sinhala Word Generator 92

6.4.3 Online Search module 93

6.5 Summary 94

Chapter 7 BEES in Action 95

7.1 Introduction 95

7.2 BEES as an Online Translator 95

7.3 BEES as a Web Page Translator 97

7.4 BEES as a Selected Sentence Translator 100

7.5 BEES as a Desktop Application 102

7.6 Summary 106

Chapter 8 Evaluation 107

8.1 Introduction 107

ix

x

8.2 Evaluation of MT systems 107

8.3 BEES Evaluation 109

8.4 Stage1: Module Testing 110

8.4.1 English Morphological Analyzer 110

8.4.2 English Parser 111

8.4.3 English to Sinhala Base Word Translator 112

8.4.4 Sinhala Morphological Generator 113

8.4.5 Sinhala Sentence Composer 114

8.4.6 Transliteration Module 115

8.5 Stage 2: Performance Testing 115

8.6 Stage 3: Accuracy Testing 117

8.7 Result of the Experiments 118

8.8 Summary 121

Chapter 9 Conclusion and Further Work 122

9.1 Introduction 122

9.2 Revisited Objectives 122

9.3 Limitations 124

9.4 Further Works 124

9.5 Summary 125

References 126

Appendix A: English Morphological analyzer- Test plan 135

Appendix B: Conjugation Table for Sinhala Language 137

Appendix C: Context-Free Grammar for Sinhala Language 143

Appendix D: Finite State Transducer for Sinhala Transliteration 145

Appendix E: Sample Evaluation form 147

Appendix F: Sample of evaluators Comments 148

List of Figures

Figure 2.1: Architecture for a rule-based machine translation system 13

Figure 4.1: Finite State Automata for Kaputu Ganaya 54

Figure 4.2: Parser tree for the sample sentence 56

Figure 5.1: Design of the BEES 61

Figure 5.2: FST for Vowels in model 1 transliteration 64

Figure 5.3: Design of the three supporting module 67

Figure 6.1: The Intermediate Editor 83

Figure 7.1: Web based architecture for the BEES 95

Figure 7.2: User interface of the Online BEES 96

Figure 7.3: A web page translator 97

Figure 7.4: BEES as a web page translator 100

Figure 7.5: Selected sentence translator 101

Figure 7.6: Desktop screen for selected sentence translation 101

Figure 7.7: User interface of the BEES 103

Figure 8.1: English Morphological analyzer with test results 111

Figure 8.2: Sinhala word conjugator 114

Figure 8.3: User interface of the evaluation test bed 116

Figure 8.4: Online evaluation form 117

Figure 8.5: Translation accuracy 121

xi

xii

List of Tables

Table 2.1: Existing Machine translation systems 26

Table 3.1: Regular and irregular forms of the English Noun 29

Table 3.2: English Noun Morphological rules 30

Table 3.3: English verb Morphology 31

Table 3.4: Morphological rules for English Verbs 32

Table 3.5: Tense patterns (Active voice) 33

Table 3.6: The Sinhala Alphabet 36

Table 3.7: Vocalic Stokes and their position 37

Table 3.8:The consonant l with vocalic stokes 37

Table 3.9: Sample case makers in Sinhala 40

Table 3.10: conjugation table for we;a ganaya 41

Table 3.11: Inflection form of the Sinhala verbs (Active) 42

Table 3.12: Inflection form of the Sinhala verbs (Passive) 43

Table 4.1: Paradigm table for Kaputu Ganaya 54

Table 6.1: Grammatical notations for the English Dictionary 84

Table 8.1: Sample test plan for English Morphological analyzer 110

Table 8.2: Sample test plan for English parser 112

Table 8.3: Sample Sinhala Morphological rules 113

Table 8.4: Results for module testing 119

Table 8.5: Human evaluation results 120

Table 8.6: Accuracy results 120

Table 8.7: Final evaluation results 121

.

1

Chapter 1

INTRODUCTION

1.1 Preamble

A Natural Language is a kind of marvelous artifact ever invented by mankind. It is a

cornerstone of all kinds of communications. Each natural language plays the role of

describing thoughts of humans in a particular environment. As such, a natural

language has a strong bearing on the culture and the environment within which a

certain community of persons live. This is why we identify large number of different

natural languages worldwide. Despite the differences in languages, people still want

to communicate with persons who use different languages. Differences in languages

have become a barrier for cross-cultural communications. In particular, many nations

have not been able to access a huge reservoir of world knowledge written in English,

unless those nations have a sound knowledge in English. On the other hand, people

do not know English will not be able to contribute to the world knowledge. It is

undisputable the importance of mother tongue for discovery and creation of new

systems of knowledge. Consequently, this has resulted in what is called language

barrier for communication. In fact, this issue is not only between English and other

languages, but also between any two languages.

Of course, people have been practicing a solution for the issue. That is nothing but

translation between two languages by knowing the both languages. However, can we

really expect everyone to know every language? Undoubtedly, this is impractical.

The emergence of digital computer technology in early 1950s had postulated the

concept of machine translation to seek assistance from computers to seek solutions

for long felt language needs of humans. Since then hundreds of research works have

been conducted to translate between natural languages. The machine translation has

been a branch of Natural Language Processing, which comes under the broad area of

Artificial Intelligence. It is commonly cited that machine translation has been one of

2

the least achieved area in Artificial Intelligence over the last sixty years. As such, a

generic approach to machine translation has been an unrealized dream of researchers.

Thus, machine translation approaches have become so much language specific.

1.2 English to Sinhala Machine Translation

This thesis presents a research conducted to develop English to Sinhala machine

translation system. Sinhala is one of the Indo Aryan family languages and it is the

spoken language of 74% of the people in Sri Lanka. Sinhala has also been one of the

constitutionally recognized official languages of Sri Lanka [53]. Numbers of

Statistical results show that, more than 80% of Sinhala spoken community does not

have the ability to read and write in English [46][126]. While encouraging the

learning of English, one also cannot devalue the importance of mother tongue for

discovery of knowledge for the betterment of mankind.

In the Asian region, many countries including India, Thailand, Malaysia and Japan

have conducted considerable amount of research in machine translation. Despite Sri

Lanka has been working on various projects in machine translation, still little behind

as compared with similar researches conducted in the Asian region. Weerasinghe

[154] has pioneered machine translation research in Sri Lanka. Thus, this project will

contribute to extend machine translation initiatives in Sri Lanka. The project presents

a theoretical-based translation approach, which would also be beneficial to machine

translation projects, which handles languages closer to Sinhala language.

Before presenting the aim and objectives of the project, a brief introduction to field

of machine translation is given in section 1.3.

1.3 What are Machine Translation Systems?

The Machine Translation system refers to computer software that translates text or

voice from one natural language into another with or without human assistance [73]

3

[154]. According to the design, each Machine translation system can be broadly

categorized into two groups, namely, the direct translation system and the indirect

translation system. The direct translation system translates source language into

target language by using word-to-word or phrase-to-phrase mapping. In contrast,

indirect translation systems use an Interlingua or some kind of transfer method. This

approach starts with an analysis of source text and performs a synthesis to generate

corresponding text in the target language. Figure 1.1 gives classic pyramid to show

relationship between these two approaches to machine translation.

Figure 1.1: Relationship between direct and indirect translations

Under the above two broad areas, several approaches have been used to develop

hundreds of machine translation systems all over the world. Among other

approaches, Human-assisted, Rule-based, Statistical, Example-based, Knowledge-

based, Hybrid, and Agent-based are commonly cited as the most successful

approaches for machine translation.

Comparing the existing machine translation systems and their approaches, many of

these systems use sequential level architecture for Natural Language Processing and

machine translation [59]. This sequence comprises of steps such as preprocessing,

4

morphological analysis, syntax analysis, semantic analysis, pragmatic analysis and

post processing.

Despite many attempts have been taken to develop machine translation systems, at

present this area has achieved very little. In fact due to ever felt need of machine

translation, some people have rushed to develop such systems without a proper

conceptual or theoretical basis for their approaches. This has resulted in creating

many machine translation systems that go through ad-hoc processes to translate

between languages. This also amounts to constraint the development in the field of

machine translation.

1.4 Aim of the Research

This thesis proposes to design and develop English to Sinhala machine translation

system with a theoretical basis.

1.5 Objectives of the Research

In order to reach the above aim, the following key objectives have been identified.

These objectives range from critical review of existing approaches to machine

translation to evaluation of the proposed theoretical-based approach to machine

translation.

Objective 1

Critically review the existing systems, concepts and tools for machine

translation.

Objective 2

Develop a Computational grammar for Sinhala Language

Objective 3

Design and develop English to Sinhala Machine Translation system

5

Objective 4:

Evaluate the system

1.6 1.5 Scope of the Project

The scope of the project is limited to develop a computational grammar for Sinhala

language as per concept of Varanegeema to handle most commonly used 27-noun

forms and 36 verb forms.

1.7 Hypothesis

In order to achieve the above aim and objectives, the hypothesis employed in the

thesis can be stated as concepts of Varanegeema (Conjugation) in Sinhala

languages can be used to drive English to Sinhala Machine translation.

1.8 Structure of the Thesis

The thesis has been structured with nine chapters. The following is the structure of

the thesis with a brief explanation of the contents of each chapter.

Chapter 1 has provided an overall introduction to the whole research project. It

briefly explained the research problem addressed in the thesis, overview for machine

translation, aim, objectives and the hypothesis employed in the thesis.

Chapter 2 reports on the literature survey on Machine Translation with a detailed

description leading to highlight the problem addressed in the thesis. Also this chapter

provides a detailed study about the state of the art Natural Language Processing by

describing different approaches adapted.

6

Chapter 3 is on an overview of the English and Sinhala languages as per

Morphology, Syntax and Semantic concerns of the both languages. This chapter also

gives a compression between English and Sinhala languages by showing issues

related to machine translation.

Chapter 4 discusses the novel approach taken to develop English to Sinhala machine

translation system. It presents the hypothesis of the project in the first place. Then the

chapter explains the mechanism of the translation process, nature of input, output and

key features of the system.

Chapter 5 is about the design of the proposed English to Sinhala Machine Translation

system. Each and every module of the design model is explained separately by

describing the functionality and relation among the modules.

Chapter 6 presents the implementation of the English to Sinhala machine translation

system. This chapter gives implementation details about prolog-based modules, java

based user interface, Intermediate editor and ontology of the lexical databases.

Chapter 7 presents how BEES works in practice when translating a given English

text. This chapter also explains applications of BEES as, a standalone translator, an

on demand translator, web page translator and selected text translator for machine

translation.

Chapter 8 reports evaluation of the English to Sinhala machine translation. The

evaluation methodology, evaluation steps, participants and the result of the

evaluation are also given in this chapter.

Chapter 9 concludes the thesis by referring to achievement of each objective. The

chapter also presents limitations and further work of the research conducted.

7

1.9 Summary

This chapter provided an overview for the entire project by describing the problem

to be addressed, aim, objectives and the hypothesis employed in the thesis. It briefly

explained the proposed English to Sinhala Machine Translation. Structure of the rest

of the thesis has also been presented in the chapter.

The next chapter reports on critical review of the existing approaches to machine

translation together with major machine translation systems that are based on these

approaches.

8

Chapter 2

STATE OF THE ART OF MACHINE TRANSLATIONS

2.1 Introduction

The previous chapter presented an overview of the thesis. This chapter gives the state

of the art of Natural language processing with a special attention on the Machine

Translation. Some of the related fundamental aspects in Machine Translation will

also be discussed in this chapter.

2.2 Fundamentals of the Natural Language Processing

The Natural Language Processing (NLP) is a field of computer science and

linguistics concerned with the interactions between computers and human (Natural)

languages [107]. It is also a sub field of Artificial Intelligence (AI) in the area of

Computer Science [128]. According to many electronic resources, the history of the

Natural language processing began with the Turing article named Computing

Machinery and Intelligence [151]. It is known as the Turing test as a criterion of

intelligence. After that, In 1957 Noam Chomsky in the academic and scientific

community as one of the fathers of modern linguistics, introduced the Syntactic

Structures for grammar [31]. It is recognized as a most important text in the field of

linguistics. After that, it becomes fundamental theory for Natural Language

Processing and many of these Machine Translation systems use this syntactic

structure [31][33].

The Natural language processing has come under broad area of the field of Artificial

Intelligence. The NLP is used to do several tasks including machine translation,

automatic summarization, Information retrieval, optical character recognition, speech

recognition, text-to-speech etc [107][128][147].

Based on the task, the Natural Language Processing systems reserved several issues

such as Natural language understanding, Natural language generation, Speech and

text segmentation, Part-of-speech tagging and the Word sense disambiguation [84]

9

2.3 Machine Translation Systems

Machine Translation system is a computer software to translate text or speech from

one natural language to another [161][162]. The Machine translation is a sub area of

the Natural language processing which is identified during early days of Artificial

Intelligent (AI). Due to various reasons associated with complexity of languages, for

more than last sixty years, Machine Translation has been identified as one of the least

achieved areas in computing [74]. These issues range from Morphological to

semantics of source and target languages.

The history of Machine Translation dates back to late 1940s. A look-up dictionary

at Birkbeck College in London has been cited as an early work of machine

translation in 1948. After that, 1950 to 1960 many researchers attended to develop

Machine Translation systems by using trial-and-error approach [75] especially for

Russian to English language. In 1950 first machine translation system was developed

to translate Russian sentences into English.

In 1958 first practical machine translation system was implemented by the IBM

Corporation to US Air force under direction of Gilbet King [76]. This system

translates Russian text into English and it successfully works until 1970. In the

meantime RAND cooperation distributed current linguistic theory and emphasized

the Statistical analysis. They were prepared bilingual glossaries with grammatical

information and the grammar rules with the first parser based on the dependency of

grammar.

In 1970, SYSTRAN [144] implemented a new Russian-English machine

translation system which is the replacement of the previous system of the US Air

force. This system translated more than 100000 pages per year. In the mean time,

many researchers were attempting to develop machine translation systems. Among

others, syntactic transfer system for English-French is one of the strong researches in

the field. Further, principal experimental effect focused on the Interlingua

approaches with more attention pays to the syntactic aspects [75].

10

In 1980, many computer companies attempted to develop computer-aided

translations especially for Japanese-English. These systems are low level direct

translation systems that are confined to morphological and syntactic analysis. After

1980 Machine translation researches were developed through many areas. Corpus-

based machine translation approach is the most popular approach until now.

However, due to the complexity of the natural languages, development of the

machine translation systems has become a research challenge. In addition, many

researchers have also noted that, Operational syntax, idioms and Universal syntactic

categories are some completely unsolved linguistic problems in the machine

translation [171].

2.4 Current Approaches to Machine Translation

Considering the translation approaches, machine translation system can be

classified into seven categories, namely, Human-assisted, Rule-based, Statistical,

Example-based, Knowledge-based, Hybrid and Agent-based. Statistical, Example

based, Knowledge based and Hybrid approaches are used copra for the machine

translation. Therefore, these approaches are named as corpus-based approach. All of

these machine translation approaches have their own strengths and weakness.

Obviously, the success rate of a translation is depended on the approach. Each

approach for the machine translation is discussed below.

2.4.1 Human-assisted Machine Translation

Human-assisted machine translation approach is an approach for the machine

translation particularly Indian families of machine translation. The human assisted

approach uses human interaction for the pre editing, post editing and/or intermediate

editing stages[85]. This approach uses human support for the semantic handling in

the machine translation. Using this human assisted approach, numbers of machine

translation systems have been developed.

11

In the Indian region a number of machine translation systems have used this

approach, including Anusaaraka, ManTra, MaTra, Angalabarathi etc [133][38][146].

Anusaaraka [4] [7] is a popular Human-assisted translation system for Indian

languages that makes text in one Indian language accessible to another Indian

language. This system uses Paninian Grammar model [6] to its language analysis.

The Anusaaraka project [16] has been developed to translate Punjabi, Bengali,

Telugu, Kannada and Marathi languages into Hindi. English-Hindi Anusaaraka

translates English text into Hindi. The approach and lexicon is general, but the

system has mainly been applied for childrens stories [95].

MaTra is a human-assisted transfer-based translation system for English to Hindi

[11]. This System uses general-purpose lexicons and applied mainly in the domains

of news. MaTra follows a structural and lexical transfer approach for its machine

translation. The MaTra aims to produce understandable output for wide coverage,

rather than perfect output for a limited range of sentences.

Mantra [106] is a machine assisted translation tool that, translates English text into

Hindi in several domains. ManTra is based on the Tree Adjoining Grammar (TAG).

The Mantra system was started with the translation of administrative documents such

as appointment letters, notification and circular issued in central government from

English to Hindi.

Angalabharti [103] is also a human-assisted machine translation system used in

India. Since India has many languages, there are a variety of machine translation

systems. For example, Angalahindi [133] translates English to Hindi using machine-

aided translation methodology. Human-aided machine translation approach is a

common feature of most Indian machine translation systems. In addition, these

systems also use the concepts of both pre-editing and post-editing as the means of

human intervention in the machine translation system.

Chandrashekhar Research Centre [20] has developed a machine aided translation

system for Tamil to Hindi. Tamil to Hindi translator is based on Anusaaraka

Machine Translation System and the input text is in Tamil and the output can be seen

12

in a Hindi text. Stand-alone, API and Web-based on-line versions are developed.

Tamil morphological analyzer and Tamil-Hindi bilingual dictionary are the

byproducts of this system [133].

In addition to the above, KSHALT is a human assisted Machine Translation

system that translates English to Korean language [85]. This translation system

contains four phrases namely English Parser, English Analyzer, English to Korean

transfer and the Korean generation.

2.4.2 Rule-based Machine Translation

Rule-based approach is yet another approach for machine translation. This

approach gives grammatical correct translation by using set of rules. Basically, the

rule-based machine translation system contains a source language morphological

analyzer, a source language parser, translator, target language morphological

analyzer, target language parser and several lexicon dictionaries. Source language

morphological analyzer analyzes a source language word and provides the

morphological information. Source language parser is a syntax analyzer that analyzes

source language sentences. Translator is used to translate a source language word

into target language. Target language morphological analyzer works as a generator

and it generates appropriate target language words for the given grammatical

information. Also target language parser works as a composer and it composes a

suitable target language sentence. Furthermore, this type of machine translation

system needs minimum of three dictionaries namely the source language dictionary,

the bilingual dictionary and the target language dictionary. Source language

morphological analyzer needs a source language dictionary for morphological

analysis. Bilingual dictionary is used by the translator for translating source language

into target language; and the target language morphological generator uses the target

language dictionary to generate target language words. Figure 2.1 can present general

architecture of the rule-based machine translation system.

13

A number of machine translation systems have been designed through the rule-

based approach. Among others Apertium [18] is a rule-based Machine Translation

system, which translates related languages. This is an opensource system that can

be used to translate any related two languages. The Apertium engine follows a

shallow transfer approach and consists of the eight pipelined modules, such as de-

formatter, A morphological analyzer, A parts-of-speech (PoS) tagger, A lexical

transfer module, A structural transfer module, A morphological generator, A post-

generator, and A re-formatter.

Source Language Morphological

Analyzer

Source language

Dictionary

Source Language parser

Bilingual translator

Target language Morphological

generator

Target language sentence generator

Target Language

Target language

Dictionary

Dictionary

Bilingual

Source language

Figure 2.1: Architecture for a rule-based machine translation system

Toshiba [145] is another Rule-based Machine translation system for English to

Japanese vice versa. To translate a given source text, system uses Morphological

analysis, Syntax analysis, translation word selection and structural transformation,

syntax transformation and morphological generation steps. This system can translate

open-domain written texts by using rule-based. This system uses three dictionaries

namely common word dictionary, a technical-term dictionary and a user-defined

14

dictionary. The common word dictionary includes both English-Japanese and

Japanese- English translation. The technical term dictionary includes domain-specific

technical terms. They have used user defined dictionary to store user provided

information such as unknown word information.

Further, rule-based machine translation approaches can be categorized as three

groups namely transfer-based, Interlingua and dictionary based. The transfer based

and Interlingua approach has same idea for translation. Both two approaches used

intermediate representation that captures the "meaning" of the original sentence

[10][84][56]. The difference between both approaches is the interlingua-based

system uses language independent intermediate representation and transfer-based

system uses language dependent intermediate representation. Most of these machine

translation systems include Morphological analysis, lexical categorization, lexical

transfer, Structural transfer and Morphological generation. The dictionary based

machine translation system uses dictionary for its machine translation with or

without Morphological or syntax analysis. These type of Machine Translation

systems ideally suitable to translate long lists of phrases. Numbers of machine

translation systems have been developed under the above three border headings.

2.4.2.1 Transfer-based Machine Translation

Lavie and others [96] have applied transfer based approach to the Hindi-to-English

translation system named Xferand. It trained under the extremely limited data

scenario. This Xfer system uses IIITMorpher (Morphological analyzer) [79] to

analyze Hindi words with the root and the other features such as gender, number, and

tense. The Xfer system uses 70 transfer rules including a rather large verb paradigm,

with 58 verb sequence rules, ten recursive noun phrase rules and two prepositional

phrase rules. They have noted that, this approach is particularly suitable for

languages with very limited data resources.

Arabic to English machine translation system has been developed through the

Transfer-based approach [120]. This system is named as Npae-Rbmt. The Npae-

15

Rbmt is used an intermediate representation that captures the meaning of the

original sentence in order to generate the correct translation. This system has

evaluated through the 88 thesis titles and journals from the computer science domain.

The accuracy of the result was 94.6%.

Apertium platform follows a transfer-based machine translation model [18]. Using

these shallow-transfer approach Swedish to Danish machine translation system has

been developed [125]. Swedish to Danish machine translation system uses two

morphological dictionaries to analysis and generation. This is the first free software

translator of Swedish to Danish.

Using Affix-Transfer-based approach, Tagalog-to-Cebuano [170] Unidirectional

Machine Translator system has been developed. The morphological analysis is based

on TagSA (Tagalog Stemming Algorithm) and is focused on an affix

correspondence-based POS (parts-of-speech) tagger.

Opentrad is an open source transfer based Machine translation system intended for

related language pairs and not so similar pairs [3][48]. The Opentrad uses different

translation methods according to each language pair. For related languages it uses

shallow transfer, even though for nonrelated pairs the system uses deep transfer [49].

Opentrad also uses open-source machine translation engine[101] (Matxin) as the

translation engine.

OpenLogos is the Open Source version of the Logos Machine Translation System

[122]. It is one of the earliest and longest running commercial machine translation

products in the world. This system accepts documents in various formats and

produces high quality translations [136]. OpenLogos translates from English and

German to the major European languages, including Spanish, Italian, French and

Portugese.

2.4.2.2 Interlingua Machine Translation

The Interlingua approach gives language independent meaning representation for the

source language to target language translation. The Interlingua gives one single

meaning representation for all the languages and it has been reserved as an extremely

16

difficult task in practice [135]. However, there are several advantages in the

Interlingua approach. Among others Interlingua gives more easy way to adding new

language than all other methods. Also it seems several disadvantages. Meaning

representation is the critical approach in Interlingua. If the meaning is too simple

then meaning will be lost in the translation. On the other hand it is too complex and

analysis and generation will be too difficult.

Numbers of Machine translation system have been developed through the Interlingua

approach. Abdelhadi and others have been developed English to Arabic machine

translation system based on Interlingua approach [1]. They have used mapping

system to Arabic to intermediate representation. This mapping system contains three

steps namely, selecting lexical items for each Interlingua concepts, mapping the

semantic roles and mapping the semantic features for each Interlingua concept to

appropriate syntactic feature in the feature structure.

Among others ICENT is the interlingua-based Chinese-English natural language

translation system [167]. This system introduces the realization mechanism of

Chinese language analysis, which contains syntactic parsing and semantic analyzing

and gives the design of Interlingua in details.

Tai to English machine translation system is another successful machine

translation system for Tai to English [29]. This system translates the Thai sentences

into Interlingua of a Thai LFG tree using LFG grammar and a bottom up parser.

2.4.2.3 Dictionary based Machine Translation

The dictionary based machine translation systems are commonly used for cross-

language retrieval systems [77]. This dictionary based approach uses dictionary-

based method to generate the equivalent target query for the given source language

query.

Mandal and others [105] have been developed a cross-language retrieval system

for the retrieval of English documents in response to queries in Bengali and Hindi.

17

This dictionary-based machine translation system uses to generate the equivalent

English query out of Indian language topics.

Thenmozhi and Aravindan have been developed Tamil-English Cross Lingual

Information Retrieval System for Agriculture Society [149]. This system developed

for the Farmers of Tamil Nadu which helps them to specify their information need in

Tamil and to retrieve the documents in English. It uses a Morphological Analyzer to

obtain the root terms of source query. This Machine Translation approach retrieves

the pages with mean average precision of 95%.

2.4.3 Statistical Machine Translation

Statistical machine translation approach is by far the most widely-studied machine

translation method in the field of natural language processing. This approach tries to

generate translations using statistical methods based on bilingual text corpora [84].

Using this statistical approach, large numbers of machine translation systems have

been developed.

Moses is a Statistical machine translation system that allows automatically train

translation models for any language pair [108]. The Moses system has several

features. It offers two types of translation models namely, phrase-based and tree-

based. Moses system uses factored translation models, which enable the integration

linguistic and other information at the word level.

Babel Fish [168] is a web-based application developed by AltaVista which

translates text or web pages from one language into another. The translation

technology for Babel Fish is provided by SYSTRAN [144], whose technology also

powers the translator at Google and a number of other sites. It can translate among

English, Simplified Chinese, Traditional Chinese, Dutch, French, German, Greek,

Italian, Japanese, Korean, Portuguese, Russian, and Spanish. A number of sites have

sprung up that used the Babel Fish service to translate back and forth between one or

more languages.

18

Bing Translator [112] is a service provided by Microsoft as part of its Bing

services which allow users to translate texts or entire web pages into different

languages. All translation pairs are powered by Microsoft Translation, developed by

Microsoft Research; it uses Microsoft's own syntax-based statistical machine

translation technology.

Google Translator [51] translates a section of text, or a webpage, into another

language. It does not always deliver accurate translations and does not apply

grammatical rules, since its algorithms are based on statistical analysis rather than

traditional rule-based analysis.

In the Indian region, Udupa and Faruquie have developed an English-Hindi

Statistical Machine Translation System [152]. This machine translation system is

based on IBM Models 1, 2, and 3. The system has been tested through the English-

Hindi parallel corpus consist of 150,000 sentence pairs.

Singh and Bandyopadhyay have been developed Manipuri-English bidirectional

statistical machine translation system [133]. The system uses four useful translation

factors namely case markers and POS tags information at the source side and suffixes

and dependency relations at the target side. This translation system has been

evaluated through the BLEU score.

2.4.4 Example-based Machine Translation

The example-based machine translation system uses bilingual corpus with the parcel

text for the machine translation. These systems are trained through the bilingual

parallel copra, which contain sentence pairs. The example based approach is more

useful for detecting the context from the text. Also this approach uses translation

memories [13]. Using this approach number of machine translation systems have

been developed all over the world.

19

Among others, OpenMaTrExis one of the open source Example-based machine

translation systems which is freely available on the OpenMaTrEx web site [121].

OpenMaTrEx has been developed through the marker hypothesis, which is

compressed on marker-driven chunker, a collection of chunk aligners and two

engines.

Kyoto-U is a successful Example based machine translation system that translates

English-Japanese [119]. This system uses a morphological analyzer and dependency

analyzer to detect Japanese sentence structures and converted into dependency

structures. In addition, Japanese and English parsers and bilingual dictionary were

used as external resources.

At present many researchers are researching to develop example-based machine

translation systems by using World Wide Web as parallel corpora [55]. The wEBMT

is an example-based machine translation (EBMT) system that uses the World Wide

Web as the parallel corpus [13].

2.4.5 Knowledge-based Machine Translation

Knowledge-based machine translation approach uses knowledge for machine

translation. This is an extended idea of the example-based machine translation. This

approach uses linguistic and computational instructions, which are supplied by a

human. Numbers of commercial quality Machine Translation systems have used this

knowledge-based approach. Among others EDR[150] and KANT [86] are the major

knowledge-based machine translation systems.

EDR (Electronic Dictionary Research) [114], by Japanese, is the most successful

machine translation system. This system has taken a knowledge-based approach in

which the translation process is supported by several dictionaries and a huge corpus

[115]. While using the knowledge-based approach, EDR is governed by a process of

statistical machine translation. As compared with other machine translation systems,

EDR is more than a mere translation system but provides lots of related information.

20

KANT (Knowledge-based Accurate Natural-language Translation) is a knowledge

based machine translation system for specific domain [86]. Prototype of the KANT

architecture translates French, German, and Japanese successfully. KANT is

currently being extended in a large-scale commercial application [118]. The KANT

prototype has been implemented in the domain of technical electronics manuals, and

translates from English to Japanese, French and German.

2.4.6 Hybrid Machine Translation

The Hybrid machine translation system uses combine method in rule-based and

Statistical machine translation approaches. This hybrid approach has several

advantages.

Among others, SYSTRAN is the market leading provider of language translation

software products and solutions for the desktop, enterprise and Internet that facilitate

communication in 52 language combinations and in 20 vertical domains [124].

Introducing combination of self-learning and linguistic technologies SYSTRANS has

been developed hybrid machine translation system [144] named as a SYSTEMS

Enterprise server 7.

The English to Arabic machine translation system has also been developed through

the hybrid approach, which is combined between rule-based and example based

approaches [133].

2.4.7 Agent-based Machine Translation

Agent technology, more specifically multi-agent systems, have also been used to

handle machine translations. This Multi-agent system provides tools for building

artificial Complex Adaptive Systems [131]. In general any multi agent system

contains four key components, namely Multi-Agent Engine, Virtual world, Ontology

and Interfaces [130][131]. The multi agent engine provides a run time support for

agents. The engine starts as the first step of the system. Virtual world is the

21

environment of the multi agent systems. Using this Virtual world, agents are

cooperated and competed with each other as they construct and modify the current

scene. The Ontology contains conceptual problem domain knowledge of each agent.

There are a number of NLP systems that have been developed using multi agent

system technology [175][129][130][113][36]. Most of these systems use agents to

handle semantics in the translation.

Minakow and others [113] have developed a Multi Agent-based text understanding

system for car insurance domain. This system uses Multi agent system based

approach to understand a given text. The system uses four steps to text understanding

namely morphological analysis, Syntax analysis, semantic analysis and pragmatic

analysis. To analyze the whole text is divided into sentences. Then first three stages

are applied to each sentence. After analyzing each paragraph text is passed to

pragmatic analysis.

Stefanini and others have developed a Multi-agent based general Natural language

processing system named Talisman [141]. Talisman agents can communicate with

each other without the central control. These agents are able to directly exchange

information using an interaction language. Linguistic agents are governed by a set of

local rules. The TALISMAN deals with ambiguities and provides a distributed

algorithm for conflict resolutions arising from uncertain information.

2.5 Existing English to Sinhala Machine Translation Systems

During the past few years many Sri Lankan researchers contributed to develop

Machine Translation systems for local languages. Among others University of

Colombo has recorded a significant research to develop English to Sinhala and

Sinhala-Tamil machine translation system with several Local language resources

such as Sinhala corpus [99][159], Sinhala text to Speech system [160], Parts of

Speech Tagger[45] and OCR system for Sinhala language [158]. As a first attempt

Weersinghe and others have been researching to develop Sinhala to Tamil machine

translation system through the corpus based approach [157]. This translation system

22

evaluates through the BLUE score matrix [123] and reasonable result were achieved.

At present they are researching to develop English to Sinhala machine translation

system through the translation memories[156]. They have designed translation tool

named OpenTM, which is based on the translation memories. They have mentioned

that this OpenTM is suitable for any language pairs around the world, where at least

one language requires complex script support.

Further, many other local researchers have developed several prototype English to

Sinhala machine translation systems through several approaches. In 2003, Vithanage

and others have developed English to Sinhala machine translation systems for

weather forecasting domain [153]. Vithanages translation system can translate

simple sentences and works on the limited set of words and the limited sentence

patterns. This translation system is fundamental rule-based and it has used

Paragraphs and sentence tokenization, simple parsers (English and Sinhala),

translators and Sinhala sentence generators for English to Sinhala translation.

In 2008, Fernando and others have developed English to Sinhala machine translation

system using Artificial Neural Networks [47]. A Probabilistic Neural Network is

used to identify the English grammar and it is based on Bayesian classifiers. This

system has been achieved 50% accuracy in the grammatical translation. It has been

tested through 84 test cases including 12 tenses and it only capable to translate only

the simple sentences.

In addition to above, some people all over the world have attempted to develop

machine translation system for Sinhala. Among others, Hearth and others have

attempted to develop translation system for Japanese to modern Sinhalese [57]. The

system has a limited vocabulary and it handles translations only within its domain.

2.6 Concepts and Techniques for Machine Translation

In the previous section the author has discussed several existing approaches for

Machine Translation. Many of these machine translation systems have used the

Morphological analysis and the syntax analysis to analyze the source language. This

23

Morphological analysis and syntax analysis is done by Morphological analyzers and

parsers. Morphological analyzers and parsers act the major task in any machine

translation. Therefore the following sub section gives brief description about

Morphological analysis and syntax analysis.

2.6.1 Morphological Analysis

The morphological analysis is the identification (analysis) of the structure of

morphemes and other units of meaning in a language like words, affixes, and parts of

speech [84][162][176]. Historically, the first attempt made for the morphological

analysis, was done by the ancient Indian linguist Panini, who formulated the 3,959

rules of Sanskrit morphology (Vyakarana). This Panini grammar [24] is the basis of

all the Indian families of language including, Hindi, Sinhala, Pali, Sanskrit etc. Using

this Panini grammar model, many researchers have developed number of

morphological analyzers for their language analysis [5][6].

The Morphological analyzers for English language have been developed by many

researchers. Koskenniemis two-level morphology was the first practical and most

general model in the history of computational linguistics for the analysis of

morphologically complex languages [92][93]. Koskenniemis Pascal implementation

of morphological analysis was quickly followed by others. The most influential of

them was the KIMMO system by Lauri Karttunen and his students at the University

of Texas. PC-KIMMO is yet another morphological analysis tool, which was based

on Koskenniemis work and implemented in C [87]. Among others, PC-KIMMO is

supposed to be the only available free English morphological analyzer with a wide

coverage [34]. The lexicon used in PC-KIMMO considers verb, pronoun, noun,

prepositions, adverbs and adjectives. The current version PC-KIMMO is

implemented in C and can be run on a PC [93]. The PC-KIMMO accepts an input

word from a user, and provides all possible morphological details of the word. In

addition, many European and Scandinavian countries have developed morphological

analyzers for their languages. These countries have exploited real power of

computer technology for machine translation.

24

Asian countries including India, Japan and Thailand have also developed

morphological analyzers for computer-based natural language processing [5][6]. For

example, Anusaaraka system has developed morphological analyzers for six Indian

languages [16]. Anusaaraka has been designed to translate among major Indian

languages and its morphological analysis is based on the paradigms. The Paradigm is

used both for word analysis as well as word generation. Also Akshar Bharati and

others have developed a Generic Morphological Analysis Shell that can be used to

develop morphological analyzers for different minority languages [5]. This Shell

uses finite state transducers with features to give the analysis of a given word.

Further, it integrates paradigms with augmented FSTs. The current model has been

developed for sample data of Hindi, Telugu, Tamil and Russian. The above generic

Morphological Analysis Shell uses dictionaries, s paradigm table and paradigm

classes.

2.6.2 Syntax Analysis

Syntax analysis is used to analysis structure in the text and is used to determine

whether or not a text conforms to an expected format [84][91]. In the Machine

Translation point of view, this syntax analysis is done by the Parser, which is used to

analyze the given text (sentences). To analyze the given text Parsers use several

techniques coming under Top-down and Bottom-up parsing.

The Top-down parsers are analyzing the input source left to right and searching for

parse trees using a top-down expansion [162]. Using this top-down parsing approach

there are several types of Parsers that are also developed including Recursive descent

parser, LL parser, Earley Parser and the X-SAIGA parser. These parsers have

demonstrated their own properties in addition to the top-down parsing features.

The Recursive descent parser is the straightforward forms of top-own parsing [97].

The LL Parser is also used top-down parsing and parses the input from Left to right,

and constructs a leftmost derivation of the sentence. The ANTLR [148] is the

popular LL parser, especially for compilers. The LL(k) parser uses the above

techniques to parse the sentences without backtracking. The Earley parsers are

25

especially suitable for ambiguous grammars and use for parsing the computational

linguistics. Many of these parsers are already implemented through the C, Java, Perl

and Python languages. The X-Saiga parsers are developed under the X-Saiga project

to create algorithms and implementations which enable the construction of language

processors such as recognizers, parsers, interpreters, translators, etc. they have

implemented several algorithms, at various stages to develop X-Saiga [166].

The bottom-up parser attempts to identify the most fundamental units first. Then it

attempts to build trees upwards the start. These parsers are mainly used to analyze

both natural languages and computer languages. Using this bottom-up parsing

approach several types of Parsers are also developed including Operator Precedence

parsers, LR parsers and the CYK parsers.

The operator precedence parser is a bottom-up parser that interprets an operator-

precedence grammar [162]. The LR Parser [132] is also used bottom-up parsing and

parses the input from Left to right, and constructs a rightmost derivation of the

sentence. The CYK Parsers are used CockeYoungerKasami algorithm and parsing

techniques are based on the bottom-up parsing. The CYK parsers operate on context-

free grammars given in Chomsky normal form (CNF) [31][32].

In addition to the above Parsers are developed by using several computer

languages especially prolog [25] and number of tools are used to develop parsers

including ANTLR, Yacc, JavaCC etc.By using these programming languages and

development tools numbers of parsers have been developed by many people for

several Natural languages as well as computer programming languages.

2.7 Problem Definition

The existing Machine translation systems that use the stated approaches are not

directly able to translate English text into Sinhala. Since each natural language is

built on its own building blocks and structures, two languages may not be able to

handle in the same manner. Despite some Indian languages may have common

features with Sinhala, they are not identical. On the other hand such systems do not

26

provide an underlying theory to generalize machine translations. As such, it is

impossible to figure out which building block or the structure should be exactly

customized to create English to Sinhala machine translation system. Therefore, lack

of theoretically-based approach to machine translation has led to develop ad-hoc

translation systems.

2.8 Summary

This chapter gave a detailed discussion about Machine Translation systems and the

approaches used. The table 2.1 shows selected successful machine translation

systems with language pair, approach and system type.

Table 2.1: Existing Machine translation systems

System Language pair Approach & Type

Anusaaraka Among Indian languages Human-Assisted, Application

Angalabarath English to Indian

languages

Human-Assisted, Rule-based,

Application

AngalaHindi English to Hindi Machine-aid, Rule-based/ example-

based, Web based

ManTra English to Hindi Human-aided, web based

English to Urdu

MT

English to Urdu Example based, Application

Matra English to Hindi Human-aided, transfer-based

Application

Google TR Several languages Statistical, Web-based

Bable fish Several languages Systran technology, Web based

Yahoo TR Several languages Statistical, web-based

Aprtium Related languages Rule-based, Application

EDR English/Japanese Knowledge based, Application

27

According to the literature survey, the author has identified that human assisted and

rule-based approaches are more suitable for none-related language pairs such as

English and Sinhala. Next chapter reviews features of English and Sinhala languages

with a view to identify issues related to machine translation from English to Sinhala.

28

Chapter 3

OVERVIEW OF THE ENGLISH AND SINHALA LANGUAGES

3.1 Introduction

The previous chapter discussed in detail about the Machine Translation systems. The

author has pointed out issues in adapting an existing translation system for

constructing English to Sinhala machine translation system. The literature review

also revealed that the development of the Machine Translation system absolutely

depends on the structure of the source and the target languages. Therefore, this

chapter studies about language primitives and structures of English and Sinhala

languages. This study would help to provide an insight about how the translation

from English to Sinhala can be done.

3.2 The English Language

English is the international communication language and more than 53 countries are

already using it as an official language. It is a West German language that originated

from the Anglo-Frisian and Old Saxon dialects brought to Britain [162]. English

language contains 26 letters with 5 vowels [116]. The English language has eight

parts of speech such as Noun, Adjective, Pronoun, Verb, Adverb, Preposition,

conjunction and Interjection [8][165]. Rest of the section describes Morphology,

Syntax, and Semantics of the English Language.

3.3 The English Language Morphology

Morphology is the study of the way words are built up from smaller meaning bearing

units called morphems that often define as the minimal meaning-bearing unit in a

language [84]. For example the word boy consists single morpheme and the word

boys consists two morphemes namely boy and the -s.Furher, in the Morphological

view point there are two types of morphemes such as stems and affixes. In the

29

previous example a morpheme boy is a stem and the s is an affix. These stems and

affixes are participated both inflection and derivation of the word which is called

word formation [109].The Inflection provides various forms of any single word such

as Singular, Plural etc. (E.g. singular man, plural men in English). Derivation creates

new words from old ones. (E.g. the creation of dogcatcher from dog, catch and

er is a derivational process) [117][84]. Comparing the other Indo-European

languages, English grammar has minimal inflections. Therefore, the English

morphology is simpler than the other Indo-European languages. With the exception

of pronouns, English words have relatively few forms.

3.3.1 English Noun Morphology

English Noun contains two types of inflections such as number and possessive case.

Nouns generally have only two forms for Number inflection such as singular and

plural. In the possessive case, the words usually end in ( s ) or ( ) for example

boys and boys.

The English noun participates regular and irregular inflections. The regular inflection

gives general forms of the singular, plural and possessive cases. Table 3.1 shows

regular and irregular nouns with the inflection forms.

Table 3.1: Regular and irregular forms of the English Noun

Grammar rule Regular Irregular

Singular boy Man

Plural boys Men

Singular Possessive boy's man's

Plural Possessive boys' men's

Considering the morphology of the English noun, it has very limited number of

rules for noun inflections. The table 3.2 shows some morphological rules for the

30

English Noun. Basically, the plural noun is formed by adding some suffixes to the

singular noun such as s, es, ies, ves etc. The posessive case is formed by adding s

or s.

Table 3.2: English Noun Morphological rules

English Noun Morphology

No Morphological structure Base word Example

1 Singular noun Boy boy

2 Plural Base + s Boy Boys

3 Plural Base + es Class Classes

4 Plural Base y + ies Baby Babies

5 Plural Base f + ves Knife Knives

6 Singular Possessive Base + s School Schools

7 Plural Possessive Plural + Boy Boys

3.3.2 English Verb Morphology

English verb contains five types of inflection namely Infinitive, simple present, past

tense, past participle and present participle. In regular verbs, 3rd person singular ends

with s, past tense and past participle ends with ed and the present participle ends

with ing. Note that English has a large number of irregular verbs and these verbs do

not fit with this pattern. The personal pronoun has different forms depending on

number (singular and plural), case (subject, object, possessive, etc.), and person (1st,

2nd and 3rd person). In the 3rd person singular, there is gender too. The table 3.3

shows the entire verb forms available for the English verb play (Regular) and eat

(Irregular).

The Morphological point of view, English regular verbs have several

morphological rules. The table 3.4 shows Morphological rules for English verb.

Most of the English regular verbs have simple inflection rule. However, Irregular

31

verbs use different patterns. Then the regular verbs expect simple present (adding s)

and the Present Participle (adding ing) forms.

3.3.3 English Adjective Morphology

Adjectives have comparative and superlative forms namely comparative adjectives

are end with 'er') and the superlative adjectives end with 'est'). For example; higher

and highest are the comparative and superlative forms of the adjective high. Other

parts of speech; adverb, preposition, conjunction and Interjection do not show

inflections.

Table 3.3: English verb Morphology

English Verb Morphology

Morphological structure Regular verb Irregular

verb

Infinitive play eat

Past played ate

Present Participle playing eating

Past Participle played eaten

Present:

I play eat

You play eat

He, She, It plays eats

We play eat

You play eat

They play eat

32

Table 3.4: Morphological rules for English Verbs

English Verb Morphology

No Morphological structure Regular verb Irregular verb

1 Infinitive verb (Base verb) play eat

2 Simple present (base + s) plays eats

3 Past(base + ed) played ate

4 Present Participle (base + ing) Playing eating

5 Past Participle (Base +ed) played eaten

3.4 Syntax of the English Language

The syntax is the study of the rules that gives the structure of the sentences [162].

English Language has its own format and it differs from the Sinhala language syntax.

The below section gives a brief description about English sentence syntax, which is

based on the scientific psychin web site [172][174]. English language contains four

main sentence types namely declarative, Interrogative, Imperative and conditional.

The English sentence may be simple or compound. The compound sentences consist

of two or more simple sentences joined by conjunctions.

The declarative sentence consists of a subject and a predicate. The subject may be

a simple subject or a compound subject. A simple subject consists of a noun phrase

or a nominative personal pronoun. Compound subjects are formed by combining

several simple subjects with conjunctions. All the sentences in this paragraph are

declarative sentences.

Interrogative sentences are used to form questions. One form of an interrogative

sentence is a declarative sentence followed by a question mark and there are several

ways available for Interrogative sentences that start with what, who, which etc.

The Imperative sentences are commands; consist of predicates that only contain

verbs in infinitive form. Generally, imperative sentences are terminated with an

exclamation mark instead of a period.

33

The Conditional sentences are used to describe the consequences of a specific

action, or the dependency between events or conditions. Conditional sentences

consist of an independent clause and a dependent clause.

In addition to the above, deep structural analysis needs to develop machine

translation for English source sentence analysis specially, subject, object, predicate

and sentence patterns. These information are very useful to develop English Phrases.

3.4.1 The English Sentence Subject

The subject is the part of the sentence that performs an action or which is

associated with the action. The subject may be simple or compound. The Simple

subject may be a noun phrase or a nominative personal pronoun. (The nominative

personal pronouns are: I, you, he, she, it, we and they)

3.4.2 The English Predicate

The predicate is the part of the sentence that contains a verb or verb phrase and its

complements. English has three main kinds of verbs: auxiliary verbs, linking verbs,

and action verbs.

3.4.3 Verb Tense

Verb tenses are inflectional forms of verbs or verb phrases that are used to express

time distinctions [8]. The table 3.5 shows the structure of some common tenses.

Table 3.5: Tense patterns (Active voice)

Tense Example

Simple present I write a book

The boy sings a new song

Present I am writing a book

34

continuous The boy is singing a new song

Present perfect I have written a book

The boy has sung a new song

Present perfect continuous

I have been writing a book

The boy has been singing a new song

Past tense I wrote a book

The boy sang a new song

Past continuous I was writing a book

The boy was singing a new song

Past perfect I had written a book

The boy had sung a new song

Past perfect continuous

I had been writing a book

The boy had been singing a new song

Future tense I will write a book

The boy will sing a new song

Future continuous I shall be writing a book

The boy will be singing a new song

Future perfect I shall have written a book

The boy will have sung a new song

Future perfect continuous

I shall have been writing a book

The boy will have been singing a new song

3.4.4 The Complement

The predicate consists of a verb or verb phrase and its complements, if any. A verb

that requires no complements is called intransitive. A verb that requires one or two

complements is called transitive.

35

3.5 Semantics of English Language

Semantics is the study of the meaning. It typically focuses on the relation between

signifiers, such as words, phrases, signs and symbols, and what they stand for [162].

Semantics can be classified as three groups namely, word level meaning sentence

level meaning and the paragraph level meaning.

3.5.1 Word Level Semantics

Word level semantics means semantics may define by the words in the sentence. As

an example consider the following sample sentences, This is a red rose, this paper

is red, and the supervisor flashes the red light for his student. The word red

gives different meaning in each sentence.

3.5.2 Sentence Level Semantics

The sentence level semantics refers to the meaning that depended on the sentence.

Analyzing the sentence level semantics of the sentence is very important for many

areas [37].

3.5.3 The paragraphs Level Semantics

The paragraphs level semantic analysis [173] is a solution for the word sense

ambiguity [80]. Further, many of the researchers have done researches to analyze

paragraphs level semantics [127].

3.6 The Sinhala Language

The Sinhala Language is constitutionally recognized as the official language of Sri

Lanka, along with Tamil. Sinhala is the mother tongue of the Sinhalese. Sinhala

language has its own writing system, which is an offspring of the Brahmi script [22].

36

Maldives, Dhivehi are the closest relative languages to Sinhala. Further, Sinhala

scripts are the worlds 16th most creative alphabet among todays functional

languages [35]. The Sinhalese most historical book Mahavansa [102] noted that, the

prince Vijaya and his entourages who came from India in the 5th century BC were

merged with the native Hela tribes known as Yakka and Naga who spoke Elu

language (the ancient form of the Sinhalese language) and the new nation called

Sinhala came to exist with the Sinhala language.

Further, Sinhala differs from all other Indo-Aryan languages. It contains a pair of

vowel sounds that are unique to it, such as short vowel: we ae and Long vowel:

wE aae. Also Sinhala contains a set of five nasal sounds known as half nasal or

prenasalized stops. These sounds as represented in modern Sinhala writing and

their Romanized notations are as follows: a (nng), `ca (ndj), ` (nnd), |a (nd), (mb)

[88].

The next sub section briefly describes the Sinhala alphabet, morphology and the

syntax of the Sinhala language.

3.6.1 Sinhala Alphabet

The Sinhala alphabet consists of 61 letters comprising 18 vowels, 41 consonants and

2 semi-consonants [40][22].These symbols represent 40 sounds: 14 vowel sounds

and 26 consonant sounds. This is quite similar to other Indic alphabets, as all of

them appear to be offshoots of the Sanskrit alphabet [50]. Table 3.6 shows the

Sinhala alphabet.

Table 3.6: The Sinhala Alphabet

Letter Type Sinhala Letters

Vowels w, wd, we, wE, b, B, W, W! ,, iD, iDD, t, ta, ft, T, , T!

Consonants

l, L, . , >, V, , p, P, c, Cv [, {, P, g, G, v, V, K,

, ; , : , o, O, k, |, m, M, n, N, u, U, h, r, ,, j, Y, I, i,

y,

37

Furthermore, some graphical symbols, stokes, are used in conjunction with

consonants. They are used in writing some vowels too (example. wd" ta" ft). Unlike

in English, a stoke may be positioned at any of the four sides of the base letter.

Table 3.7 shows Sinhala stokes and their positions [42].

Table 3.7: Vocalic Stokes and their position

No Stoke Name Position Example

1 A Al-lakuna1 Upper ia

A Al-lakuna2 Upper

2 D Aela-pilla Right ld

3 E Kettiaedapilla Right le

4 E Digaaedapilla Right lE

5 S Ketti ispilla Upper ls

6 S Diga ispilla Upper lS

7 Q Kettipaa pilla1 Lower nq

= Kettipaa pilla2 Lower l=

8 Q Digapaa pilla1 Lower nQ

+ Digapaa pilla1 Lower l+

9 D Gaettapilla Right iD

10 f Kombuva Left fu

11 ! Gayanukitta Right T!

In addition to above, Sinhala letters (characters) are generated using vowels,

consonants and conjunction with consonant and stokes. Table 3.6 shows the

combination of the consonant l (k) with vocalic stokes.

Table 3.8:The consonant l with vocalic stokes

No Character Letter

1 la la

2 la + w l

38

3 la + wd ld

4 la + we le

5 la + wE lE

6 la + b ls

7 la + B lS

8 la + W l=

9 la+ W! l+

10 la + iD lD

11 la + iDD lDD

12 la + t fl

13 la + ta fla

14 la + ft ffl

15 la + T fld

16 la + flda

17 la+ T! fl!

3.7 Sinhala Language Morphology

Sinhala is an inflationary rich language and it participates inflection, derivation and

conjugation for nouns and verbs. Inflection is the modification of a word to express

different grammatical categories such as tense, mood, voice, aspect, person, number,

gender and case [54]. The Derivation is "Used to form new words, as with happiness

and un-happy from happy, or determination from determine [162] and conjugation

refers to the creation of derived forms of a verb from its principal parts by inflection

Conjugation may be affected by person, number, gender, tense, aspect, mood, voice,

or other grammatical categories. A table giving all the conjugated variants of a verb

in a given language is called a conjugation table or a verb paradigm.

3.7.1 Sinhala Noun Morphology

The Sinhala Noun is a word that represents the noun, pronoun and the adjective in

the English language. The Sinhala noun has four types of inflections such as Gender

39

(lingaya), Number (Wachana), Person (Purusha) and Case (Vibhakthi). There are

three genders namely masculine gender, feminine gender and neuter gender. Singular

and plural are the Number and there are three persons namely first person

(Uthtamapurusha) second person (Maddamapurusha) and third person

(prathamapurusha). Also there are nine cases in Sinhala such as Nominative

(prathama), Accusative (karma), Instrumental (kaththru), Auxiliary (karana), Dative

(sampadana), Ablative (avadhi), Genitive (Sambanda), Locative (adara) and

Vocative (alapana) [54][134]. There are 27 inflection forms generated for single base

noun such as nine Vibhakthi, article and the number. For example Sinhala base word

.j inflects as .jhd, .jfhda, .jfhla etc. The base word is directly affected by

the nine cases. Some case suffixes are written with the base word and some are

written separately. Table 3.9 shows sample case makers of the Sinhala noun. There

are number of case maker forms available in Sinhala that depends on the gender of

the noun.

From morphological point of view, a Sinhala noun is also a word, and nouns are

participated inflection and derivations. The Sinhala nouns can be divided into thr