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Machine translation has yet to reach its potential within the translation market as a whole. Figures suggest that MT accounts for a small us $ 100 million portion of a us $ 10 billion translation market (Intelligence 2004). Many have suggested that the reason is the poor quality of results, hence it only makes sense when very large amounts of data need to be processed (Oren 2004). For the MT market to expand, it is necessary to improve the quality of results, which will then make it a viable alternative within the much bigger translation market. Arabic is acquiring attention in the natural language processing (NLP) community be- cause of its political importance and the linguistic differences between it and European languages. These linguistic characteristics, especially complex morphology, present in- teresting challenges for NLP researchers. According to Holes (2004) Arabic is the sole or joint official language in twenty independent Middle Eastern and African states: Alge- ria, Bahrain, Egypt, Iraq, Jordan, Kuwait, Lebanon, Libya, Mauritania, Morocco, Oman, Palestine, Qatar, Saudi Arabia, Somalia, Sudan, Syria, Tunisia, the United Arab Emirates and Yemen. Since the end of the nineteenth century, there have been large communities of Arabic speakers outside the Middle East, particularly in the United States and Eu- rope. Arabic is also the language of Islam’s holy book the Qur’an, and as such is the religious language of all Muslims. Arabic has been an official language of the United Nations alongside English, French, Spanish, and Chinese since 1 January 1971’(Holes 2004). There are a number of different Arabic words in languages such as Persian, Turk- ish, Urdu or Malawian. The words derived from Arabic that exist in Spanish, Portuguese, German, Italian, English or French are also numerous (Bateson 2003). The aim of this research is to create an Interlingua Machine Translation (MT) system that will accept Arabic source sentences and generate English sentences, and to build a 2
high-quality translation technology that is adequate for text-to-text translation. In this research we build an Interlingua architecture in MT which translates efficiently. We con- sider semantic analysis and other disambiguation related to Arabic. This research also represents a starting point for the future implementation of a successful and complete Arabic MT engine. The hypothesis under investigation and main aims are to present an interlingua architecture, which is not only successful in translating simplex Arabic (intransitive, transitive, ditransitive and copula-like nominative) sentences to corresponding English sentences, but also does so in the most optimal way. This research is the first contribution (not just for Arabic) that uses the Role and Refer- ence Grammar (RRG) model as a basis for machine translation. This contribution shows how RRG can be used to deduce the logical structure of sentences and produce a lexical representation which can then be used as the interlingua bridge. The lexicon in RRG takes the position that lexical entries for verbs should contain unique information only, with as much information as possible derived from general lexical rules. This was the reason for creating our own lexicon since we need an RRG–based lexicon of the unique information of verbs and their logical structure. UniArab stands for Universal Arabic machine translator system. The UniArab system is a natural language processing application based on Role and Reference Grammar for translating the Arabic language into any other language, using an RRG based interlingua bridge. The UniArab system can understand the part of speech of a word, agreement features, number, gender and the word type. The syntactic parse unpacks the agreement features between elements of the Arabic sentence into a semantic representation (the logical structure) with the ‘state of affairs’ of the sentence. In the UniArab system we intend to have a strong analysis system that can unpack all information and its attributes. This 3
Page 1 and 2: A generic framework for Arabic to E
Page 3 and 4: sentence and its linking algorithm
Page 5 and 6: Declaration I herby certify that th
Page 7 and 8: advices, but also academically and
Page 9 and 10: CONTENTS 2.3.1.2 Indefinite nouns .
Page 11 and 12: CONTENTS 4.4.1 Non-Roman alphabet s
Page 13 and 14: 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: 1 Introduction The following paragr
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 and 66: 3.6. SUMMARY there will be more com
Page 67 and 68: 4.1. ADVANTAGES OF MACHINE TRANSLAT
Page 69 and 70: 4.2.3 Lexical databases 4.2. COMPUT
Page 71 and 72: 4.3.2 Direct translation 4.3. BASIC
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4.3. BASIC MACHINE TRANSLATION STRA
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would also be needed for an interli
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4.4.1 Non-Roman alphabet scripts 4.
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Figure 4.5: NP rule (NP -> det n pp
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4.5. CHALLENGES OF ARABIC TO ENGLIS
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Figure 4.7: Direct MT system 4.6.2
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4.7 Summary Figure 4.9: Structure t
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5 Design of Arabic to English machi
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5.2. DESIGNING AN XML LEXICON ARCHI
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5.3. DESIGN OF TEST STRATEGY Table
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5.4. DESIGN OF EVALUATION CRITERIA
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6 UniArab: a proof-of-concept Arabi
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6.1. CONCEPTUAL STRUCTURE OF THE UN
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6.2. UNIARAB: LEXICAL REPRESENTATIO
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6.3. UNIARAB: GENERATION which mean
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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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