Parallels in complex verbal forms denoting tense, mood and aspect 4.3.2.2. Parallel structures in verb phrase 4.3.2.1. Parallels in demonstratives 4.3.1.2.Parallels in quantifiers 4.3.1.3. Parallel structures of English and Tamil pharases 4.3.1. Parallels in comparative clause of adverbs 4.2.5. Parallels in comparative clause of quantity 4.2.4.3. Parallels in comparative clause of quality 4.2.4.2. Parallels in comparative clauses 4.2.4.1.
Parallels in nomainal/complement clause 4.2.2. Parallel clause structures of English and Tamil 4.2.1. Paralles in negative pronouns and determiners 4.1.4. Parallels in negation in non-equative sentences 4.1.3.3. Parallels in negation in equative sentences 4.1.3.2. Parallels in interrogative sentences 4.1.2.1. Parallel sentential structures in English and Tamil 4.1.1. Summary Chapter 4 Parallel Structure of English and Tamil Language 4.0 Introduction 4.1. Creation of parallel corpus for the SMT system 3.6.6.1.
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POS Annotation 3.6.5.5.1 POS Tagset 3.6.5.5.1.1 Principles for Designing Linguistic Standards for Corpora Annotation 3.6.5.5.2 Super Set of POS Tags 3.6.5.5.3 Super Set of POS Tags for Indian Languages 3.6.5.5.4 Manual POS Annotation 3.6.6. Data storage, maintenance and dissemination 3.6.5.4. Creating multilingual parallel corporat for Indian languages 3.6.5.1. Corpora creation in Indian languages 3.6.4.1. Concordance 3.5.4 Lexical Collocation 3.5.5 Key Word In Context (Kwic) 3.5.6 Local Word Grouping (Lwg) 3.5.7 Word Processing 3.5.8 Tagging 3.6. Relevance of the present research workģ.3. Previous research works 1.6 Chaterization 1.7.
Motivation 1.2 Issues in the research 1.3 Aims and objectives of the work 1.4 Methodology 1.5. A standard reference 67 CONTENT Chapter 1 Introduction 1.1. Sampling and representativeness 65 3.2.2. Corpus in the present day context 64 3.2.1. Introduction 15 2.1 Machine Translation 16 2.1.1 Machine Translation System for non Indian languages 17 2.1.2 Machine Translation Systems for Indian languages 28 2.2 History of Machine Translation 37 2.3 Need for MT 42 2.4 Problems in MT 42 2.5 Types of Machine Translation Systems 43 2.6 Different Approaches used for Machine Translation 44 2.6.1 Linguistic or Rule-Based Approaches: 44 2.6.1.1 Direct MT System: 45 2.6.1.2 Interlingua Machine Translation 47 2.6.1.3 Transfer based MT 49 2.6.2 Non-Linguistic Approaches 50 2.6.2.1 Dictionary Based Approach 50 2.6.2.2 Empirical or Corpus Based Approaches 51 2.6.2.2.1 Example Based Approach 51 2.6.2.2.2 Statistical Approach 52 2.6.3 Hybrid Machine Translation Approach 54 2.7 Categories of Machine Translation System 54 2.7.1 Fully Automated Machine Translation System 55 2.7.2 Machine Aided Translation System 55 2.7.3 Terminology Data Banks 56 2.8 Advantages of Statistical Machine Translation over Rule Based 56 Machine Translation 2.9 Applications of Machine Translation 58 2.10 Summary 63 Chapter 3 Creation of Parallel Corpus 63 3.0 Introduction 63 3.1 Pre-Electronic corpus 63 3.2. PAGE NOS 9 9 10 12 13 13 14 15 15 Chapter 2 Survey of MT systems in India and Abroad 15 2.0. The systems built by some of us are inferor to what is available in Google. Google has come out with a comparatively good MT system for English-Tamil translation. SMT is the outcome of such short-cut method. Computer scientist want to get of rid of linguistics. But unfortunbately nobody has the patinence for it. A rule based system making use of the theory of linguistics is a wecome thing. After some time the entire exercise will be irrelevant. With here consent I am presenting it here. I thought of editing it and publish as a book. Vasuki for her PhD work under my supervision. of India for the technological development of Indian Languages. I got opportunity to work for a few MT projects funded by Govt. Rajendran Sankaravelayuthan Ī FEW WORDS I have been working in the field of MT with special referece to Tamil since 1990. ENGLISH TO TAMIL MACHINE TRANSLATION SYSTEM USING PARALLEL CORPUSĭr.
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