Anaphora resolution for Arabic machine translation :a case study of nafs

PhD ThesisIn the age of the internet, email, and social media there is an increasing need for processing online information, for example, to support education and business. This has led to the rapid development of natural language processing technologies such as computational linguistics, information retrieval, and data mining. As a branch of computational linguistics, anaphora resolution has attracted much interest. This is reflected in the large number of papers on the topic published in journals such as Computational Linguistics. Mitkov (2002) and Ji et al. (2005) have argued that the overall quality of anaphora resolution systems remains low, despite practical advances in the area, and that major challenges include dealing with real-world knowledge and accurate parsing. This thesis investigates the following research question: can an algorithm be found for the resolution of the anaphor nafs in Arabic text which is accurate to at least 90%, scales linearly with text size, and requires a minimum of knowledge resources? A resolution algorithm intended to satisfy these criteria is proposed. Testing on a corpus of contemporary Arabic shows that it does indeed satisfy the criteria.Egyptian Government

Phoneme-based statistical transliteration of foreign names for OOV problem.

Gao Wei.Thesis (M.Phil.)--Chinese University of Hong Kong, 2004.Includes bibliographical references (leaves 79-82).Abstracts in English and Chinese.Abstract --- p.iAcknowledgement --- p.iiiBibliographic Notes --- p.vChapter 1 --- Introduction --- p.1Chapter 1.1 --- What is Transliteration? --- p.1Chapter 1.2 --- Existing Problems --- p.2Chapter 1.3 --- Objectives --- p.4Chapter 1.4 --- Outline --- p.4Chapter 2 --- Background --- p.6Chapter 2.1 --- Source-channel Model --- p.6Chapter 2.2 --- Transliteration for English-Chinese --- p.8Chapter 2.2.1 --- Rule-based Approach --- p.8Chapter 2.2.2 --- Similarity-based Framework --- p.8Chapter 2.2.3 --- Direct Semi-Statistical Approach --- p.9Chapter 2.2.4 --- Source-channel-based Approach --- p.11Chapter 2.3 --- Chapter Summary --- p.14Chapter 3 --- Transliteration Baseline --- p.15Chapter 3.1 --- Transliteration Using IBM SMT --- p.15Chapter 3.1.1 --- Introduction --- p.15Chapter 3.1.2 --- GIZA++ for Transliteration Modeling --- p.16Chapter 3.1.3 --- CMU-Cambridge Toolkits for Language Modeling --- p.21Chapter 3.1.4 --- Re Write Decoder for Decoding --- p.21Chapter 3.2 --- Limitations of IBM SMT --- p.22Chapter 3.3 --- Experiments Using IBM SMT --- p.25Chapter 3.3.1 --- Data Preparation --- p.25Chapter 3.3.2 --- Performance Measurement --- p.27Chapter 3.3.3 --- Experimental Results --- p.27Chapter 3.4 --- Chapter Summary --- p.28Chapter 4 --- Direct Transliteration Modeling --- p.29Chapter 4.1 --- Soundness of the Direct Model一Direct-1 --- p.30Chapter 4.2 --- Alignment of Phoneme Chunks --- p.31Chapter 4.3 --- Transliteration Model Training --- p.33Chapter 4.3.1 --- EM Training for Symbol-mappings --- p.33Chapter 4.3.2 --- WFST for Phonetic Transition --- p.36Chapter 4.3.3 --- Issues for Incorrect Syllables --- p.36Chapter 4.4 --- Language Model Training --- p.36Chapter 4.5 --- Search Algorithm --- p.39Chapter 4.6 --- Experimental Results --- p.41Chapter 4.6.1 --- Experiment I: C.A. Distribution --- p.41Chapter 4.6.2 --- Experiment II: Top-n Accuracy --- p.41Chapter 4.6.3 --- Experiment III: Comparisons with the Baseline --- p.43Chapter 4.6.4 --- Experiment IV： Influence of m Candidates --- p.43Chapter 4.7 --- Discussions --- p.43Chapter 4.8 --- Chapter Summary --- p.46Chapter 5 --- Improving Direct Transliteration --- p.47Chapter 5.1 --- Improved Direct Model´ؤDirect-2 --- p.47Chapter 5.1.1 --- Enlightenment from Source-Channel --- p.47Chapter 5.1.2 --- Using Contextual Features --- p.48Chapter 5.1.3 --- Estimation Based on MaxEnt --- p.49Chapter 5.1.4 --- Features for Transliteration --- p.51Chapter 5.2 --- Direct-2 Model Training --- p.53Chapter 5.2.1 --- Procedure and Results --- p.53Chapter 5.2.2 --- Discussions --- p.53Chapter 5.3 --- Refining the Model Direct-2 --- p.55Chapter 5.3.1 --- Refinement Solutions --- p.55Chapter 5.3.2 --- Direct-2R Model Training --- p.56Chapter 5.4 --- Evaluation --- p.57Chapter 5.4.1 --- Search Algorithm --- p.57Chapter 5.4.2 --- Direct Transliteration Models vs. Baseline --- p.59Chapter 5.4.3 --- Direct-2 vs. Direct-2R --- p.63Chapter 5.4.4 --- Experiments on Direct-2R --- p.65Chapter 5.5 --- Chapter Summary --- p.71Chapter 6 --- Conclusions --- p.72Chapter 6.1 --- Thesis Summary --- p.72Chapter 6.2 --- Cross Language Applications --- p.73Chapter 6.3 --- Future Work and Directions --- p.74Chapter A --- IPA-ARPABET Symbol Mapping Table --- p.77Bibliography --- p.8