1. تمرين شماره 1 درس NLP سيلابس درس NLP در دانشگاه هاي ديگر ___________________________ راحله مکي استاد درس: دکتر عبدالله زاده پاييز 85

2. University of Cmbridge-2006 IntroductionBrief history of NLP research, current applications, generic NLP system architecture, knowledge-based versus probabilistic approaches. Finite-state techniques Inflectional and derivational morphology, finite-state automata in NLP, finite-state transducers. Prediction and part- of-speech tagging Corpora, simple N-grams, word prediction, stochastic tagging, evaluating system performance. Parsing and generation Generative grammar, context-free grammars, parsing and generation with context-free grammars, weights and probabilities. Parsing with constraint-based grammars Constraint-based grammar, unification. Compositional and lexical semantics Simple compositional semantics in constraint-based grammar. Semantic relations, WordNet, word senses, word sense disambiguation. Discourse and dialogue Anaphora resolution, discourse relations. ApplicationsMachine translation, email response, spoken dialogue systems.

3. Stanford University ______________________________________  Introduction to and history of NLP  Syntax  the basics  Syntax: chart parsing  Syntax: transition network parsing  Probability  N-gram models  Probabilistic algorithms  Probabilistic context-free grammars  Experimental design, information extraction  Semantics  The basics  tbd

4. Stanford University (cont.) ___________________________________________  Learning extraction patterns Handout  Transformation-based learning  Semantics  named entity recognition  word sense disambiguation  Discourse and pragmatics  Semantics: spreading activation techniques  Conceptual dependency theory Handout  Conceptual knowledge structures Handout  Applications  question answering  spoken language understanding  information retrieval  machine translation

5. Columbia University Week 1Introduction and Course Overview Natural Language and Formal Language: Regular Expressions and Finite State Automata Week 2Words and Their Parts: Morphology Word Construction and Analysis: Morphological Parsing Week 3Words: Tokenization and Spelling N-grams and Language Models Week 4Word Classes and POS Tagging Machine Learning Approaches to NLP Week 5Formal Grammars Parsing with Context Free Grammars Week 6Probabilistic and Lexicalized Parsing Representing Meaning Week 7Semantic Analysis Week 8Lexical Semantics: Word Sense Disambiguation Lexical Semantics: Word Relations

6. Columbia University (cont.) Week 9Lexical Semantics: Semantic Roles Robust Semantics and Information Extraction Week 10 Week 11 TBA Pronouns and Reference Resolution Week 12Text Coherence and Discourse Structure Machine Translation Week 13 Week 14 Natural Language Summarization Dialogue Systems Week 9Natural Language Generation Lexical Semantics: Semantic Roles Week 10Robust Semantics and Information Extraction TBA Week 11Pronouns and Reference Resolution Week 12Text Coherence and Discourse Structure Machine Translation

7. University of Birmingham (2005/2006) ______________________________________  Techniques of automatic speech processing & representation  Cognitive models of spoken word recognition  Phonology  Corpus techniques  Language and the Corpus  Statistical Techniques in NLP  Meaning  Word and sentence meaning  Electronic Dictionaries and Lexicography  Pragmatics and Discourse Processing  Context & Meaning  Metaphor & Metonymy

8. Washington University-2006 (Advanced Statistical Methods in Natural Language Processing) Week Topic 1 Introduction FSA and HMM 2 Supervised Learning I: - Decision tree - Decision list 3 Supervised Learning II - TBL 4 Supervised Learning III - Bagging 5 Supervised Learning IV - System Combination - Boosting Week Topic 6 Supervised Learning IV - MaxEnt 7 Semi-supervised Learning I - Self-training (Bootstrapping) 8 Semi-supervised learning II: - Co-training Unsupervised Learning I - The EM algorithm (Part 1) - Forward-backward algorithm 9 Unsupervised Learning II - Inside-outside algorithm - The EM algorithm (Part 2)