A generic framework for Arabic to English machine ... - Acsu Buffalo

More documents

Recommendations

Info

4.2. COMPUTATIONAL TECHNIQUES IN MT and the data used for doing the actual translation can be developed separately. In reality this is a little simplistic, but there are certain examples of MT systems that operate in a similar manner for different sets of data like dictionaries and grammar rules (Hutchins and Somers 1992). 4.2.1 System design As in standard software engineering, recent trends are towards modular and incremental system design. Whereas previously, systems would be built in a monolithic structure, with some debugging access into the system, now we build systems up in stages, com- pletely defining and testing each stage, before incorporating it into the overall system. This method has revolutionarised software engineering and enabled much more effective collaborative design, as well as the integration of other people’s work in any design. 4.2.2 Interactive systems Interactivity is a key aspect of computer systems. MT systems can take advantage of interactivity to achieve higher quality results. It is possible for an MT system to ask the user to select from a set of possible solutions. It is also possible to extend the lexicon through user input at the time of translation. The system might flag unfamiliar words, which the user can then categorise for inclusion in the lexicon. However, intereactivity and relying on user input can have disadvantages. For example, should the user be relied upon to be correct in his input? Is he fully aware of the linguistic properties of the words? Furthermore, as more user input is required, the benefits of MT over human translation become less significant. 48
4.2.3 Lexical databases 4.2. COMPUTATIONAL TECHNIQUES IN MT A key component of any rule-based MT system is its lexical resources; the information associated with individual words. The field of computational lexicography is concerned with creating and maintaining computerised dictionaries. In practice, rule-based MT systems can often have different dictionaries, some containing the core entries, and others containing specialised vocabulary. An MT lexicon is different from a standard dictionary, and so is typically concentrated on some linguistically homogeneous set of words, e.g. abstract nouns, intransitive verbs, or the terminology of a specialist field. It is a good investment to develop tools which aid lexicographers to expand the lexicon. 4.2.4 Tokens and tokenization The term “token” refers to an abstraction for the smallest unit in a text that is considered when describing the syntax of a language. A process of tokenization can be used to split the sentence into word tokens. Although the following example is given as XML there are many ways to represent tokenized input. The sentence He went to the school. could be tokenised as follows: He went to the school 49
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 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 and 66: 3.6. SUMMARY there will be more com
Page 67: 4.1. ADVANTAGES OF MACHINE TRANSLAT
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.
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
Page 123 and 124:
Table 7.2: Test : Verb-subject; agr
Page 125 and 126:
Figure 7.4: Verb-subject agreement
Page 127 and 128:
Table 7.5: Test : gender-ambiguous
Page 129 and 130:
Table 7.7: Test : Verb ‘to be’
Page 131 and 132:
Table 7.9: Test : Verb ‘to have
Page 133 and 134:
7.2. SENTENCE TESTS Table 7.11: Tes
Page 135 and 136:
7.2.6 Pro-drop Arabic Table 7.13: T
Page 137 and 138:
Table 7.15: Test : Intransitive 2 A
Page 139 and 140:
7.2.7.3 Ditransitive Table 7.17: Te
Page 141 and 142:
Table 7.19: Test : Ditransitive wit
Page 143 and 144:
7.2. SENTENCE TESTS In a case where
Page 145 and 146:
7.3 System evaluation 7.3. SYSTEM E
Page 147 and 148:
7.3. SYSTEM EVALUATION search engin
Page 149 and 150:
Now is not the end. It is not the b
Page 151 and 152:
8.1. THESIS SUMMARY and so we hope
Page 153 and 154:
and proving the suitability of the
Page 155 and 156:
References Abn-Aqeal, 2007. sharah
Page 157 and 158:
REFERENCES Salem, Y., Hensman, A.,
Page 159 and 160:
Appendix 139
Page 161 and 162:
• Yasser Salem and Brian Nolan. 2
Page 163 and 164:
Arabic Letter and Phonetic Value Le
Page 165 and 166:
C List of translatable sentences I
Page 167 and 168:
she is an engineer. Zaid loves Fati
Page 169 and 170:
Jack is eating with the spoon.
Page 171 and 172:
Ayah tore the page. ֓āyh
Page 173 and 174:
Eman saw Sarah. r֓at ֓iymā
Page 175 and 176:
Carl is brushing his hair. ym
Page 177 and 178:
I want a book. ֓aryd ktāb
Page 179 and 180:
Omar gave the book to Khalid.
Page 181 and 182:
D Verbs in lexicon Verbs in Arabic
Page 183 and 184:
Arabic Example Logical Structure
Page 185 and 186:
Arabic Example Logical Structure
Page 187 and 188:
Arabic ys. rs. r Example the wheel
Page 189 and 190:
Arabic ֓adrs Example I am teach
Page 191 and 192:
Package: Name of Class Class Summar
show all

A generic framework for Arabic to English machine ... - Acsu Buffalo

You also want an ePaper? Increase the reach of your titles

Delete template?

Save as template?