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Lesson 1 Introduction to Overview
Welcome, motivations, what is Natural Language Processing, hands-on demonstrations. Ambiguity and uncertainty in language. The Turing test. Course outline and logistics.
o    Introduction 
o    Examples of Text 
o    Funny Sentences 
o    Administrative 
o    Why is NLP hard?     

Lesson 2: Introduction to Parts of Speech, Morphology and the Lexicon, ACLO 
This topic will cover Parts of Speech, Morphology, Text Similarity, and Text Preprocessing. Parts of speech 
o    Morphology and the Lexicon 
o    Text Similarity: Introduction 
o    Morphological Similarity: Stemming 
o    Spelling Similarity: Edit Distance 
o    NACLO 
o    Preprocessing 

Lesson 3: NLP Tasks and Text Similarity
This topic will cover Vector Semantics, Text Similarity, and Dimensionality Reduction. I will also go through a long list of sample NLP tasks (e.g., Information Extraction, Text Summarization, and Semantic Role Labeling) and introduce each of them briefly.
o    Semantic Similarity: Synonymy and other Semantic Relations 
o    Thesaurus-based Word Similarity Methods 
o    The Vector Space Model 
o    Dimensionality Reduction    

Lesson4: Syntax and Parsing, Part 1
This topic will cover the basics of Syntax and Parsing, including CKY parsing and the Earley parser.
o    Syntax 
o    Parsing 
o    Classic Parsing Methods 
o    Earley Parser 
o     The Penn Treebank

Lesson 5:   Syntax and Parsing, Part 2 
This topic is related to parsing, including Statistical, Lexicalized, and Dependency Parsing as well as Noun Sequence Parsing, Prepositional Phrase Attachment, and Alternative Grammatical Formalisms.
o    Parsing Introduction and recap/Parsing noun sequences 
o    Prepositional phrase attachment 1/3 Prepositional phrase attachment 2/3 Prepositional phrase attachment 3/3 
o    Statistical Parsing 
o    Lexicalized Parsing 
o    Dependency Parsing 
o    Alternative Parsing Formalisms    

Lesson 6: Language Modeling
This topic will cover Probabilities, Language Modeling, and Word Sense Disambiguation (WSD). 

o    Probabilities 
o     Bayes Theorem 
o    Language Modeling 1/3 
o    Language Modeling 1/3 
o    Language Modeling 2/3 
o    Language Modeling 3/3 
o    Word Sense Disambiguation 

Lesson 7: Part of Speech Tagging and Information Extraction
This topic includes the Noisy Channel Model, Hidden Markov Models, Part of Speech Tagging (all needed for the second programming assignment) and a short introduction to Information Extraction.
o    Noisy Channel Model 
o    Part of Speech Tagging 
o    Hidden Markov Models 1/2 
o    Hidden Markov Models 2/2 
o    Statistical POS Tagging 
o    Information Extraction 
o    Relation Extraction     

Lesson 8: Question Answering 
This topic will   cover different topics related to Question Answering, including Question Type Classification and Evaluation of Question Answering Systems.
o    Question Answering 
o    Evaluation of QA 
o    System Architecture 
o    QA System Architecture  

Lesson 9: Text Summarization
This topic covers Text Summarization and related topics such as Sentence Compression.
o    Summarization 
o    Summarization Techniques 1/3 
o    Summarization Techniques 2/3 
o    Summarization Techniques3/3 
o    Summarization Evaluation 
o    Sentence Simplification    

Lesson 10: Collocations and Information Retrieval
This topic covers Information Retrieval (including Document Indexing, Ranking, Evaluation), Text Classification and Text Clustering, as well as a short lecture on Collocations.
o    Collocations 
o    Information Retrieval 
o    Evaluation of IR 
o    Text Classification 
o    Text Clustering 
o    Information Retrieval Toolkits 

Lesson 11: Sentiment Analysis and Semantics
This topic covers Semantics and related topics such as Sentiment Analysis, Semantic Parsing, and Knowledge Representation.
o    Sentiment Analysis 
o    Sentiment Lexicons 
o    Semantics 
o    Representing and Understanding Meaning 
o    First Order Logic 
o    Knowledge Representation 
o    Inference 
o    Semantic Parsing     

Lesson 12: Discourse, Machine Translation, and Generation (Includes Final Exam)
This topic briefly covers Discourse Analysis, Dialogue, Machine Translation, and Text Generation.
o    Discourse Analysis 
o    Coherence 
o    Dialogue Systems 
o    Machine Translation 
o    Machine Translation Basic Techniques 
o    Machine Translation Noisy Channel Methods 
o    Machine Translation Advanced Methods 
o    Text Generation 
o    Post-course Survey