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E-raamat: Computational Linguistics, Speech And Image Processing For Arabic Language

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Computational Linguistics, Speech And Image Processing For Arabic LanguageSuurem pilt
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"This book encompasses a collection of topics covering recent advances that are important to the Arabic language in areas of natural language processing, speech and image analysis. This book presents state-of-the-art reviews and fundamentals as well as applications and recent innovations. The book chapters by top researchers present basic concepts and challenges for the Arabic language in linguistic processing, handwritten recognition, document analysis, text classification and speech processing. In addition, it reports on selected applications in sentiment analysis, annotation, text summarization, speech and font analysis, word recognition and spotting and question answering. Moreover, it highlights and introduces some novel applications in vital areas for the Arabic language. The book is therefore a useful resource for young researchers who are interested in the Arabic language and are still developing their fundamentals and skills in this area. It is also interesting for scientists who wish to keep track of the most recent research directions and advances in this area"--Back cover.
Preface v
Chapter 1 Arabic Speech Recognition: Challenges and State of the Art
1(28)
Sherif Mahdy Abdou
Abdullah M. Moussa
1 Introduction
2(1)
2 The Automatic Speech Recognition System Components
2(8)
2.1 Pronunciation lexicon
4(1)
2.2 Acoustic model
4(4)
2.3 Language model
8(1)
2.4 Decoding
9(1)
3 Literature Review for Arabic ASR
10(4)
4 Challenges for Arabic ASR Systems
14(8)
4.1 Using non-diacritized Arabic data
15(1)
4.2 Speech recognition for Arabic dialects
16(3)
4.3 Inflection effect and the large vocabulary
19(3)
5 State of the Art Arabic ASR Performance
22(2)
6 Conclusions
24(5)
References
24(5)
Chapter 2 Introduction to Arabic Computational Linguistics
29(30)
Mohsen Rashwan
1 Introduction
29(1)
2 Layers of Linguistic Analysis
30(2)
2.1 Phonological analysis
30(1)
2.2 Morphological analysis
31(1)
2.3 Syntactic analysis
31(1)
2.4 Semantic analysis
31(1)
3 Challenges Facing Human Language Technologies
32(1)
4 Challenges Facing the Arabic Language Processing
32(4)
4.1 Arabic script
33(1)
4.2 Common mistakes
33(1)
4.3 Morphological structure for the Arabic word
34(1)
4.4 Syntax of the Arabic sentence
35(1)
5 Defining the Human Languages Technologies
36(16)
5.1 Texts search (search engines)
36(2)
5.2 Machine translation
38(1)
5.3 Question answering
39(1)
5.4 Automated essay scoring
39(1)
5.5 Automatic text summarization
40(1)
5.6 Document classification and clustering
40(1)
5.7 Opinion mining
41(1)
5.8 Computer-aided language learning (CALL)
42(1)
5.9 Stylometry
42(1)
5.10 Automatic speech recognition
43(2)
5.11 Text to speech (TTS)
45(1)
5.12 Audio and video search
46(1)
5.13 Language recognition
46(1)
5.14 Computer-aided pronunciation learning
46(1)
5.15 Typewritten optical character recognition (OCR)
47(1)
5.16 Intelligent character recognition
48(1)
5.17 Book reader
48(1)
5.18 Speech to speech translation
49(1)
5.19 Speech-to-sign-language and sign-language-to-speech
49(1)
5.20 Dialog management systems
50(1)
5.21 Advanced information retrieval systems
51(1)
5.22 Text mining (TM)
52(1)
6 Arabic Computational Linguistics Institutions
52(5)
6.1 Academic institutions
52(4)
6.2 Companies interested in computational linguistics
56(1)
7 Summary and Conclusions
57(2)
References
57(2)
Chapter 3 Challenges in Arabic Natural Language Processing
59(26)
Khaled Shaalan
Sanjeera Siddiqui
Manar Alkhatib
Azza Abdel Monem
1 Introduction
59(2)
2 Challenges
61(17)
2.1 Arabic orthography
62(7)
2.2 Arabic morphology
69(3)
2.3 Syntax is intricate
72(6)
3 Conclusion
78(7)
References
79(6)
Chapter 4 Arabic Recognition Based on Statistical Methods
85(26)
A. Belaid
A. Kacem Echi
1 Introduction
85(1)
2 A Challenging Morphology
86(1)
3 Features Extraction Techniques
87(5)
4 Machine Learning Techniques
92(2)
5 Markov Models
94(9)
5.1 Case 1: Decomposition of the shape/label
94(2)
5.2 Case 2: Decomposition by association with a model
96(2)
5.3 Extension of HMM to the Plane
98(1)
5.4 Bayesian Networks
99(2)
5.5 Two Dimensional HMM
101(2)
6 Discriminative Models
103(4)
7 Conclusion
107(4)
References
108(3)
Chapter 5 Arabic Word Spotting Approaches and Techniques
111(16)
Muna Khayyat
Louisa Lam
Ching Y. Suen
1 Word Spotting
111(5)
1.1 Definition
112(1)
1.2 Input queries
113(1)
1.3 Performance measures
114(1)
1.4 Word spotting approaches
115(1)
2 Arabic Word Spotting
116(4)
2.1 Characteristics of Arabic handwriting
116(2)
2.2 Arabic word spotting approaches
118(2)
3 Databases
120(1)
4 Extracted Features
121(2)
5 Concluding Remarks
123(4)
References
123(4)
Chapter 6 A `rib --- A Tool to Facilitate School Children's Ability to Analyze Arabic Sentences Syntactically
127(28)
Mashael Almedlej
Aqil M. Azmi
1 Introduction
127(3)
2 Related Work
130(1)
3 Basic Arabic Sentences Structure
131(1)
4 System Design
132(8)
4.1 Lexical analyzer
134(1)
4.2 Syntactic analyzer
134(4)
4.3 Results builder
138(1)
4.4 Special cases
139(1)
5 Implementation
140(12)
5.1 Lexical analysis
141(4)
5.2 Syntactic analysis
145(6)
5.3 Results builder
151(1)
5.4 Output
152(1)
6 Conclusion and Future Work
152(3)
References
153(2)
Chapter 7 Semi-Automatic Data Annotation, POS Tagging and Mildly Context-Sensitive Disambiguation: The extended Revised AraMorph (XRAM)
155(14)
Giuliano Lancioni
Laura Garofalo
Raoul Villano
Francesco Romana Romani
Marta Campanelli
Ilaria Cicola
Ivana Pepe
Valeria Pettinari
Simona Olivieri
1 Introduction
155(1)
2 Description of XRAM
156(9)
2.1 Flag-selectable usage markers
157(3)
2.2 Probabilistic mildly context-sensitive annotation
160(1)
2.3 Lexical and morphological XML tagging of texts
161(2)
2.4 Semi-automatic increment of lexical coverage
163(2)
3 Validation and Research Grounds
165(1)
4 Conclusion
166(3)
References
166(3)
Chapter 8 WeightedNileULex: A Scored Arabic Sentiment Lexicon for Improved Sentiment Analysis
169(18)
Samhaa R. El-Beltagy
1 Introduction
169(1)
2 Related Work
170(2)
3 The Base Lexicon
172(1)
4 Assigning Scores to Lexicon Entries
173(5)
4.1 Data collection
173(1)
4.2 Collecting term statistics
174(1)
4.3 Term scoring
174(4)
5 Experiments and Results
178(6)
5.1 The sentiment analysis system
179(1)
5.2 The used datasets
180(1)
5.3 Experimental results
181(3)
6 Conclusion
184(3)
References
184(3)
Chapter 9 Islamic Fatwa Request Routing via Hierarchical Multi-Label Arabic Text Categorization
187(16)
Reda Zayed
Mohamed Farouk
Hesham Hefny
1 Introduction
187(3)
2 Related Work
190(1)
3 Islamic Fatwa Requests Routing System
191(4)
3.1 Text preprocessing
191(2)
3.2 Feature engineering
193(1)
3.3 The HOMER algorithm
194(1)
4 Performance Evaluation
195(4)
4.1 Data description
195(2)
4.2 Methods
197(1)
4.3 Results and Discussion
197(2)
5 Future Work and Conclusion
199(4)
References
200(3)
Chapter 10 Arabic and English Typeface Personas
203(28)
Shima Nikfal
Ching Y. Suen
1 Introduction
203(1)
2 Literature Review of Typeface Personality Studies
204(3)
3 Arabic Typeface Personality Traits
207(10)
3.1 Research methodology
207(5)
3.2 Statistical analyses of survey results
212(5)
4 English Typeface Personality Traits
217(8)
4.1 Research methodology
217(4)
4.2 Statistical analyses of survey results
221(4)
5 Summary of English Typefaces
225(1)
6 Summary of Arabic Typefaces
226(1)
7 Comparison of Both Studies
226(1)
8 Conclusions and Future Work
227(4)
References
228(3)
Chapter 11 End-to-End Lexicon Free Arabic Speech Recognition Using Recurrent Neural Networks
231(18)
Abdelrahman Ahmedy
Yasser Hifny
Khaled Shaalan
Sergio Toral
1 Introduction
231(1)
2 Related Work
232(1)
3 Arabic Speech Recognition System
233(6)
3.1 Acoustic model
234(3)
3.2 Language model
237(1)
3.3 Decoding
237(2)
4 Front-End Preparation
239(2)
4.1 Converting the Arabic text to Latin (transliteration process)
239(1)
4.2 Converting the transcription to alias
240(1)
4.3 Speech features extraction
240(1)
5 Experiments
241(4)
5.1 The 8-hour experiment
241(1)
5.2 The 8-hour results
242(2)
5.3 The 1200-hour experiment
244(1)
5.4 The 1200-hour results
245(1)
6 Conclusion
245(4)
References
246(3)
Chapter 12 Bio-Inspired Optimization Algorithms for Improving Artificial Neural Networks: A Case Study on Handwritten Letter Recognition
249(18)
Ahmed A. Ewees
Ahmed T. Sahlol
1 Introduction
249(3)
2 Neural Networks and Bio-inspired Optimization Algorithms
252(3)
2.1 Neural Networks (NNs)
252(1)
2.2 Particle Swarm Optimization (PSO)
252(1)
2.3 Evolutionary Strategy (ES)
252(1)
2.4 Probability Based Incremental Learning (PBIL)
253(1)
2.5 Moth-Flame Optimization (MFO)
253(2)
3 Swarms Working Mechanism
255(2)
4 The Proposed Approach
257(1)
5 Experiments and Results
258(6)
5.1 Dataset description
258(1)
5.2 Evaluation criteria
259(1)
5.3 Results and discussions
259(5)
6 Conclusion and Future Work
264(3)
References
265(2)
Index 267
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