Natural LanguageProcessing PPt SLIDES

Natural Language Processing
Instructor: Rada Mihalcea
Textbook: Speech and Language Processing
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Lecture

Course overview [ppt]

Short Perl tutorial (I) [ppt]

Short Perl tutorial (II) [ppt]

Linguistics Essentials [ppt]

Language Models [ppt]

Language Models [ppt]

Language Models [ppt]

Collocations [ppt]

Morphological Processing [ppt]

Word classes and part of speech tagging ppt]

Word classes and part of speech tagging ppt]

HMM Tagging. Viterbi Algorithm. [ppt]

Context Free Grammars [ppt]

Parsing with Context Free Grammars [ppt]

Probabilistic Parsing [ppt]

Word Sense Disambiguation (1) [ppt]

Word Sense Disambiguation (2) [ppt]

Word Sense Disambiguation (3) [ppt]

Text semantic similarity [ppt]

Special topics: Subjectivity and sentiment analysis [ppt]

Special topics: Subjectivity and sentiment analysis [ppt]

Special topics. Logic form transformation [ppt]

Natural Language Processing

Natural Language Processing 

Textbook required for puchase or reference (on library reserve, Barker P98.J87 2009):
Jurafsky, D. and Martin, J.H., Speech and Language Processing
2nd edition, Prentice-Hall: 2008.   

Download slides here

Topic	Slides & Reference Readings
Introduction: walking the walk, talking the talk	• Lecture 1 pdf slides; pdf 4-up; Jurafsky & Martin (JM), ch. 1. • If you don't know Python, read the NLTK book, ch. 1-3; otherwise, skim NLTK book, chs 2–3. • Background Reading (for RR 1): Jurafsky & Martin ch.4 on ngrams. (pp. 83-94; p. 114-116) • Background Reading (for RR 1): Abney on statistics and language. • Background Reading (for RR 1): Chomsky, Extract on grammaticality, 1955. • (Optional) Background chapters on NLP from Russell & Norvig, ch. 22.
RR1 discussion; Bayes' rule and smoothing; from words to parsing	Intro to Competitive Grammar Writing
Parts of speech, parsers, and statistical parsing; Parsing & competitive grammar writing I	• Bring notebook computer to class (at least 1 per team) • Competitive Grammar Writing slides, pdf; pdf, 2up • JM, ch. 13 (parsing), pp. 427-435; ch. 14, pp. 459-467 • (Optional) NLTK book on advanced parsing (skim)
Parsing & competitive grammar writing II	• Bring notebook computer to class (at least 1 per team)
Parsing & competitive grammar writing III	• Bring notebook computer to class (at least 1 per team)
Competitive Grammar Evaluation & Wrap-up, Grammy Awards
Smoothing & language models	• Lecture 3 pdf slides; pdf 4-up • JM ch. 3
Smoothing; word parsing	• Lecture 4 pdf slides; pdf 4-up
Word parsing II	• Lecture 5 pdf slides; pdf 4-up
Context Free parsing I	• Lecture 6 pdf slides; pdf 4-up
Context-free parsing II	• Lecture 7 pdf slides; pdf 4-up
RR #2	• No slides today
Earley's algorithm	• Lecture 8 pdf slides; pdf 4-up; animation of Earley algorithm here; 4upbw Earley here.
Modern statistical parsers I	• Lecture 9 pdf slides; pdf 4-up
Treebank parsers II	• Lecture 10 pdf slides;
Semantics I: the lambda calculus view	•Lecture 11 pdf slides; pdf 4-up
Semantics II: SQL	•Lecture 12 pdf slides; pdf 4-up
Semantics III: Quantifiers	• Lecture 13 pdf slides; pdf 4-up
Semantics IV: learning words	•Lecture 14 pdf slides; pdf 4-up
	•Lecture 15 pdf slides; pdf 4-up
Lexical Semantics	• Lecture 16 pdf slides; pdf 4-up
Discourse	• Lecture 17 pdf slides pdf 4-up
Language Learning	• Lecture 18 pdf slides; pdf 4-up
Language Learning & Language Change	• Lecture 19 pdf slides; pdf 4-up
Evolution of language	• Lecture 20 pdf slides; pdf 4-up
Evolution of language	• Lecture 21 pdf slides; pdf 4-up

Natural Language Processing

Course description:

This course will cover traditional material, as well as recent advances in the theory and practice of natural language processing (NLP) - the creation of computer programs that can understand, generate, and learn natural language. The course will introduce both knowledge-based and statistical approaches to NLP, illustrate the use of NLP techniques and tools in a variety of application areas, and provide insight into many open research problems.

class notes

Lecture	Reading material
Course overview [ppt]	-
Short Perl tutorial (I) [ppt]	One of the tutorials below [see the "Links" section]
Short Perl tutorial (II) [ppt]	One of the tutorials below [see the "Links" section]
Linguistics Essentials [ppt]  Guest lecture by Ben Leong	Chap.3 [Manning & Schutze] or any book on English grammar
Language Models [ppt]	Chap.4 [Jurafsky & Martin]
Language Models [ppt]	Chap.4 [Jurafsky & Martin]
Language Models [ppt]	Chap.4 [Jurafsky & Martin]
Collocations [ppt]	Chap.5 [Manning & Schutze] [a prefinal version of this chapter is available online]
Word classes and part of speech tagging ppt]	Chap.5 [Jurafsky & Martin]
Word classes and part of speech tagging ppt]	Chap.5 [Jurafsky & Martin]
HMM Tagging. Viterbi Algorithm. [ppt]	Chap.6 [Jurafsky & Martin]
Context Free Grammars [ppt]	Chap.12-13 [Jurafsky & Martin]
Parsing with Context Free Grammars [ppt]	Chap.12-13 [Jurafsky & Martin]
Probabilistic Parsing [ppt]  Guest lecture by Michael Mohler.	Chap.14 [Jurafsky & Martin]
Presentation and Workshop: "New Google Tools for Research" Mano Marks, Google  Willis 136, first floor of Library 11:30am-2pm	-
Exam preparation.	All the material studied so far.
Exam I.	All the material studied so far.
Word Sense Disambiguation [ppt]	Chap. 19, 20 [Jurafsky & Martin]
Word Sense Disambiguation [ppt]	Chap.19, 20 [Jurafsky & Martin]
Word Sense Disambiguation [ppt]	Chap.19, 20 [Jurafsky & Martin]
No class.	-
Word Sense Disambiguation	-
Special topics: Subjectivity and sentiment analysis [ppt]	-
Special topics: Subjectivity and sentiment analysis [ppt]	-
Special topics: Text semantic similarity [ppt]	-
Special topics: Text classification [ppt]	-
Thanksgiving. No class.	-
Wrap-up. Project discussions. Exam II preparation.	-
Exam II.	-
Project presentations.	-
Project presentations.	-