一、課程說明(Course Description)
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<br>This course covers the fundamentals of natural language processing with 
<br>emphases on using statistical techniques, machine learning, very large 
<br>corpora to handle unrestrict text data, in particular on-line information, 
<br>electronic communication and the World Wide Web. 
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<br>We will explore the problems and solutions in lexical, syntactic and 
<br>semantic analyses of text and show various techniques that are useful 
<br>for a wide range of applications, including machine translation, 
<br>information retrieval, and text categorization. 
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<br> 二、指定用書(Text Books)
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<br> 三、參考書籍(References)
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<br>Foundations of Statistical Natural Language Processing, 
<br>Christopher Manning and Hinrich Schutze, The MIT Press. 
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<br>Introduction
<br>1.	Fasold, Ralph and Jeff Connor-Linton. 2006. Introduction, Chapter 1, An 
<br>Introduction to Language and Linguistics
<br>2.	Mani, Inderjeet. 2006. Computational Linguistics, Chapter 14, Computation 
<br>Linguistics.
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<br>Corpora
<br>3.	J93-1001: Kenneth W. Church; Robert L. Mercer. Introduction to the Special Issue 
<br>on Computational Linguistics Using Large Corpora
<br>http://acl.ldc.upenn.edu/J/J93/J93-1001.pdf
<br>4.	J03-3001: Adam Kilgarriff; Gregory Grefenstette. Introduction to the Special Issue 
<br>on the Web as Corpus http://acl.ldc.upenn.edu/J/J03/J03-3001.pdf
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<br>Math
<br>5.	FSNLP. Chapter 2 Mathematic foundation
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<br>Language Model
<br>6.	FSNLP. Chapter 6 Statistical Inference: n-gram Models over Sparse Data
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<br>Hidden Markov Model
<br>7.	FSNLP. Chapter 9 Markov Models
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<br>Machine Translation
<br>8.	Kevin Knight. 2005. Tutorial on Machine Translation
<br>9.	"Syntax-based Language Models for Machine Translation" (E. Charniak, K. Knight, 
<br>and K. Yamada), Proc. MT Summit IX, 2003. 
<br>http://www.isi.edu/natural-language/projects/rewrite/mtsummit03.pdf.
<br>10.	"Syntax-based Alignment of Multiple Translations: Extracting Paraphrases and 
<br>Generating New Sentences" (B. Pang, K. Knight, and D. Marcu), Proc. NAACL-HLT, 2003. 
<br>http://www.isi.edu/natural-language/projects/rewrite/bopang.pdf
<br>11.	Christoph Tillmann; Hermann Ney. Word Reordering and a Dynamic Programming 
<br>Beam Search Algorithm for Statistical Machine Translation.
<br>http://acl.ldc.upenn.edu/J/J03/J03-1005.pdf
<br>Parsing
<br>12.	Eugene Charniak. 1997. Statistical Techniques for Natural Language Parsing, AI 
<br>Magazine, 18:4. 33-44.
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<br>Collocation
<br>13.	Smadja, McKeown, and Hatzivassiloglou. Translating Collocations for Bilingual 
<br>Lexicons. Smadja, McKeown, and Hatzivassiloglou. Translating Collocations for Bilingual 
<br>Lexicons. acl.ldc.upenn.edu/J/J96/J96-1001.pdf
<br>14.	Lin, Dekang and Pantel, Patrick. 2001. Dirt – discovery of inference rules from text.
<br>acl.ldc.upenn.edu/I/I05/I05-5011.pdf
<br>15.	 Rebecca Green, Bonnie J. Dorr, and Philip Resnik. Inducing Frame Semantic Verb 
<br>Classes from WordNet and LDOCE.
<br>acl.ldc.upenn.edu/acl2004/main/pdf/264_pdf_2-col.pdf
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<br>Word Sense Disambiguation
<br>16.	David Yarowsky. 1992. Word-Sense Disambiguation Using Statistical Models of 
<br>Roget's Categories. acl.ldc.upenn.edu/C/C92/C92-2070.pdf
<br>17.	David Yarowsky. 1995. UNSUPERVISED WORD SENSE DISAMBIGUATION RIVALING 
<br>SUPERVISED METHODS. acl.ldc.upenn.edu/P/P95/P95-1026.pdf
<br>18.	Dominic Widdows, Stanley Peters, Scott Cederberg, Chiu-Ki Chan. 2003. 
<br>Unsupervised Monolingual and Bilingual Word-Sense Disambiguation of Medical 
<br>Documents using UMLS. acl.ldc.upenn.edu/acl2003/nlbio/pdf/Widdows.pdf
<br>19.	Lucy Vanderwende. 1994. Algorithm for automatic interpretation of noun 
<br>sequences. Proceedings of COLING, acl.ldc.upenn.edu/C/C94/C94-2125.pdf
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<br>Question Answering and Information Extraction
<br>20.	X98-1016: Roman Yangarber; Ralph Grishman. Transforming Examples into 
<br>Patterns for Information Extraction. acl.ldc.upenn.edu/X/X98/X98-1016.pdf 
<br>21.	Heng Li. Information Retrieval by type
<br>http://lamda.nju.edu.cn/conf/mla05/reports/HangLi.Machine%20Learning%20Approaches
<br>%20to%20Information%20Retrieval.pdf#search=%22information%20%22by%20type%22%20%
<br>22hang%20li%22%20msra%22
<br>22.	Learning to find answers on the Web
<br> www.cs.columbia.edu/~eugene/papers/toit2002.pdf
<br>23.	Mitkov, R. & Ha, L.A. (2003). Computer-Aided Generation of Multiple-Choice Tests. 
<br>In Proceedings of the HLT-NAACL 2003 Workshop On Building Educational Applications 
<br>Using Natural Language Processing, Edmonton, Canada, May, pp. 17-22.
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<br>Misc
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<br>1.	Statistical Natural Language Processing Reading List
<br>ciir.cs.umass.edu/~fuchun/readlist_all/readlist.pdf
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<br> 四、教學方式(Teaching Method)
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<br> 五、教學進度(Syllabus)
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<br>1. Introduction to NLP. 
<br>2. Mathematical fundamentals: probability theory and information theory 
<br>3. Linguistics Essentials: parts of speech, phrase structure, semantics 
<br>4. Corpus-based NLP research 
<br>5. Word Sense Disambiguation 
<br>6. Lexical Acquisition 
<br>7. Markov Models 
<br>8. Part-of-speech Tagging 
<br>9. Probabilistic Context Free Grammar and Parsing 
<br>10. Text and translation alignment and Statistical Machine Translation 
<br>11. Clustering of words, use of word class in n-gram models 
<br>12. Information assess and document retrieval 
<br>13. Text categorization 
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<br> 六、成績考核(Evaluation)
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<br>Assignments
<br>Midterm examination
<br>Term project
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<br> 七、可連結之網頁位址
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<br>http://nlplab.cs.nthu.edu.tw/
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