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4 Machine translation strategies In this chapter, we introduce background information about Machine Translation. We discuss the computational techniques, basic strategies, linguistic aspects and the generation problem. Much of the background information is summarised from Hutchins and Somers (1992). Natural language processing (NLP) can be thought of as a subfield of artificial intelli- gence. It refers to understanding and automatic generation of natural human languages. Machine translation (MT) is a part of computational linguistics and refers to comput- erised systems that can translate from one natural language to another. Hence, MT uses many ideas, methods and techniques from these related fields and has also built up a body of techniques which can, in turn, be applied in other areas of computer-based language processing. 46
4.1. ADVANTAGES OF MACHINE TRANSLATION Modularity has changed as MT systems have developed. In transfer systems, lexical and structural transfer were sometimes separated. In many direct translation systems, analysis, transfer and generation are often mixed together and were not clearly distinct. As the area has matured, modularity has become an important aspect of MT systems, allowing different aspects to be developed independently. 4.1 Advantages of machine translation Some of the advantages of machine translations are as follows: • Machine translation is quicker than human translation. • It ensures consistency. There is no concern that a translator might take too much creative license with a translation or forget how a particular word was translated in earlier pages. MT will translate a particular word in the same way. However, the downside is that will exhibit the same errors over and over again. • It gives a neutral approach to translation without introducing bias, which can happen with human translators. • Machine translation is considerably cheaper. It is a one time cost; the cost of the tool and its installation. 4.2 Computational techniques in MT Computational processing allows for the analysis and processing of large amounts of data. Before looking at the computational aspects of MT, we introduce some basic concepts. Machine translation can take advantage of one of the basic concepts in computing. Since data and programs are separate, it is possible to build a program that functions with different types of data. In the case of MT, this means that the algorithms for translation, 47
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A generic framework for Arabic to E
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sentence and its linking algorithm
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Declaration I herby certify that th
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advices, but also academically and
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CONTENTS 2.3.1.2 Indefinite nouns .
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CONTENTS 4.4.1 Non-Roman alphabet s
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CONTENTS 7.2.8 Limitation of UniAra
Page 15 and 16: LIST OF FIGURES 4.4 Difference betw
Page 17 and 18: List of Tables 2.1 Dual: merely add
Page 19 and 20: LIST OF TABLES 5.9 Test strategy: p
Page 21 and 22: 1 Introduction The following paragr
Page 23 and 24: high-quality translation technology
Page 25 and 26: 1.3. TECHNOLOGIES ditransitive). Th
Page 27 and 28: 1.4. THESIS ORGANIZATION 2009b). Ch
Page 29 and 30: 2.1. CHARACTERISTICS OF THE ARABIC
Page 31 and 32: 2.2 Characteristics of Arabic words
Page 33 and 34: 2.2. CHARACTERISTICS OF ARABIC WORD
Page 35 and 36: 2.3. PART OF SPEECH INVENTORY OF TH
Page 37 and 38: 2.3.4 Verbs 2.3. PART OF SPEECH INV
Page 39 and 40: 2.3.4.2 Aspect 2.3. PART OF SPEECH
Page 41 and 42: 2.3. PART OF SPEECH INVENTORY OF TH
Page 43 and 44: 2.4.1.1 Verb and noun 2.4. SENTENCE
Page 45 and 46: Table 2.27: verb(V), subject(S) and
Page 47 and 48: 3 Role and Reference Grammar (RRG)
Page 49 and 50: 3.1. ROLE AND REFERENCE GRAMMAR LIN
Page 51 and 52: 3.2. FORMAL REPRESENTATION OF LAYER
Page 57 and 58: 3.3. NOUN PHRASE STRUCTURE appropri
Page 59 and 60: 3.4. LEXICAL REPRESENTATIONS FOR VE
Page 61 and 62: 3.5. WHY WE USE RRG AS THE LINGUIST
Page 63 and 64: 3.5. WHY WE USE RRG AS THE LINGUIST
Page 65: 3.6. SUMMARY there will be more com
Page 69 and 70: 4.2.3 Lexical databases 4.2. COMPUT
Page 71 and 72: 4.3.2 Direct translation 4.3. BASIC
Page 73 and 74: 4.3. BASIC MACHINE TRANSLATION STRA
Page 75 and 76: would also be needed for an interli
Page 77 and 78: 4.4.1 Non-Roman alphabet scripts 4.
Page 79 and 80: Figure 4.5: NP rule (NP -> det n pp
Page 81 and 82: 4.5. CHALLENGES OF ARABIC TO ENGLIS
Page 83 and 84: Figure 4.7: Direct MT system 4.6.2
Page 85 and 86: 4.7 Summary Figure 4.9: Structure t
Page 87 and 88: 5 Design of Arabic to English machi
Page 89 and 90: 5.2. DESIGNING AN XML LEXICON ARCHI
Page 95 and 96: 5.3. DESIGN OF TEST STRATEGY Table
Page 97 and 98: 5.4. DESIGN OF EVALUATION CRITERIA
Page 99 and 100: 6 UniArab: a proof-of-concept Arabi
Page 101 and 102: 6.1. CONCEPTUAL STRUCTURE OF THE UN
Page 107 and 108: 6.2. UNIARAB: LEXICAL REPRESENTATIO
Page 113 and 114: 6.3. UNIARAB: GENERATION which mean
Page 115 and 116: Figure 6.11: UniArab’s GUI 1 6.4.
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6.4.1 Lexicon interface Figure 6.13
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6.6 Summary 6.6. SUMMARY We present
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7.2. SENTENCE TESTS to which standa
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Table 7.2: Test : Verb-subject; agr
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Figure 7.4: Verb-subject agreement
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Table 7.5: Test : gender-ambiguous
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Table 7.7: Test : Verb ‘to be’
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Table 7.9: Test : Verb ‘to have
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7.2. SENTENCE TESTS Table 7.11: Tes
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7.2.6 Pro-drop Arabic Table 7.13: T
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Table 7.15: Test : Intransitive 2 A
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7.2.7.3 Ditransitive Table 7.17: Te
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Table 7.19: Test : Ditransitive wit
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7.2. SENTENCE TESTS In a case where
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7.3 System evaluation 7.3. SYSTEM E
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7.3. SYSTEM EVALUATION search engin
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Now is not the end. It is not the b
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8.1. THESIS SUMMARY and so we hope
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and proving the suitability of the
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References Abn-Aqeal, 2007. sharah
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REFERENCES Salem, Y., Hensman, A.,
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Appendix 139
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• Yasser Salem and Brian Nolan. 2
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Arabic Letter and Phonetic Value Le
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C List of translatable sentences I
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she is an engineer. Zaid loves Fati
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Jack is eating with the spoon.
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Ayah tore the page. ֓āyh
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Eman saw Sarah. r֓at ֓iymā
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Carl is brushing his hair. ym
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I want a book. ֓aryd ktāb
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Omar gave the book to Khalid.
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D Verbs in lexicon Verbs in Arabic
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Arabic Example Logical Structure
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Arabic Example Logical Structure
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Arabic ys. rs. r Example the wheel
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Arabic ֓adrs Example I am teach
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Package: Name of Class Class Summar
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