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4.3.5 Statistical machine translation 4.4. LINGUISTIC ASPECTS OF MT The ideas behind statistical machine translation come out of information theory. Essen- tially, the document is translated on the probability p(e|a) that a string e in the target language (for example English) is the translation of a string a in the source language (for example Arabic). As translation systems are not able to store all native strings and their translations, a document is typically translated sentence by sentence, but even this is not enough. We assign to every pair of strings (e|a) a number P(e|a), which we interpret as the probability that a translator, when presented with e, will produce a as its translation. You could imagine another program that takes a sentence a as input, and outputs every conceivable string e along with its P(e|a). This program would take a long time to run, even if you limit English translations to some arbitrary length. They seek the English sentence e that maximizes P(e|a) and minimizes time (Brown et al. 1993). To summarize, we compute P(e|a) by summing the probabilities of all alignments. For each alignment, we make two significant simplifying assumptions: Each English word is generated by exactly one Arabic word; and the generation of each English word is independent of the generation of all other English words in the sentence. This is clearly not true in theory. 4.4 Linguistic aspects of MT In this section we will look more closely at the kinds of linguistic problems that MT has to face and will discuss ways in which MT programs work around these problems. We will distinguish monolingual problems of morphology, lexical ambiguity, syntactic ambiguity, pragmatic aspects from bilingual problems of language contrast: lexical mismatches, structural divergence, typological differences. 56
4.4.1 Non-Roman alphabet scripts 4.4. LINGUISTIC ASPECTS OF MT Since computer technology developed mostly in English, other languages, particularly those with non-Roman alphabet have historically been seen as a special case and required new code sets to define character representations. Furthermore, not all languages with alphabetic scripts are written left-to-right, e.g. Arabic and Hebrew, so any input/output devices making this assumption will be useless for such languages. Before Unicode was standardised, there were different encoding systems for assigning this problem. Unicode provides a unique code for every character, no matter what the platform, the program and the language are. Appendix A provides the corresponding Unicode for each Arabic letter and describes the letters with their corresponding written shapes. 4.4.2 Lexical ambiguity Category ambiguities or homographs are examples of lexical ambiguities which arise when there are potentially two or more ways in which a word can be analysed. More complex are lexical ambiguities, where one word can be interpreted in more than one way. Lexical ambiguities are of three basic types: category ambiguities, homographs and transfer (or translational) ambiguities. 4.4.2.1 Category ambiguity The simplest type of lexical ambiguity is that of category ambiguity: a given word could be assigned to more than one grammatical or syntactic category (e.g. noun, verb or adjective) according to the context. There are several examples of this in English: light can be a noun, verb or adjective, also, control can be a noun or verb. In Arabic there are some words that can be in more than one category, for example ֒lā could be a preposition with meaning of “on”, or a verb with meaning of “raise”. 57
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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
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LIST OF FIGURES 4.4 Difference betw
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List of Tables 2.1 Dual: merely add
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LIST OF TABLES 5.9 Test strategy: p
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1 Introduction The following paragr
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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
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Page 65 and 66: 3.6. SUMMARY there will be more com
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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: would also be needed for an interli
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
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Page 113 and 114: 6.3. UNIARAB: GENERATION which mean
Page 115 and 116: Figure 6.11: UniArab’s GUI 1 6.4.
Page 117 and 118: 6.4.1 Lexicon interface Figure 6.13
Page 119 and 120: 6.6 Summary 6.6. SUMMARY We present
Page 121 and 122: 7.2. SENTENCE TESTS to which standa
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Page 125 and 126: 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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