Computational Linguistics Syllabus

Course Outline

Linguistics 581

Day

Reading

Assignment

Lecture

Background

Code

Mon Jan 27 Chapter 1 and Section 2.1 of Chapter 2. Jurafsky and Martin (J&M) History of Computational Linguistics. Regular Expressions and Unix demo. Assignment 1. Some history. Textbook intro What is Computational Linguistics?  
Mon Feb 03 Chapter 2, 2.1-2.4 Finite-State Automata Assignment 2 Installing Python. Textbook Ch. 2 slides Non-deterministic automata, epsilon transitions    
Mon Feb 10 Reading: Sections 3.1-3.3 of Chapter 3. Introducing words and word parts. Sections 3.4-3.9 of Chapter 3. Selected exercises from Ch. 3 Textbook Ch. 3 slides Introduction to transducers: Relations on strings Introduction to transducers    
Mon Feb 17 Section 3.10 of Chapter 3. Spelling Correction. Introduction to Viterbi algorithm. Chapter 4: J&M. 4.1-4.3. Word counting, frequency dictionaries, simple ngram models, the training corpus Spelling correction assignment Spelling correction, Brief probability intro    
Mon Feb 24 Chapter 4: J&M (ctd) Ngrams. Introduction to NLTK NLTK assignment, using Pylab, importing NLTK corpora. Lecture, Entropy and Cross-Entropy. Entropy as expected information value; cross-entropy as an evaluation tool (pdf, ps)    
Mon Mar 03 Section 4.1-4.5 of chapter 4. Practicalities. Sections 4.5.3 Chapter 4. Sections 4.4 and 4.5.1, 4.5.2 Chapter 4. Smoothing, Add-1 smoothing, Kneser-Ney smoothing. Unknown words. The Pollard assignment and smoothing assignment are due next week. Lecture. Smoothing Lecture. Kneser-Ney Lecture.    
Mon Mar 10 5.1-5.4. Word-class and part of speech tagging. Rule-based taggers. Tagging Assignment. Lecture. Tagging slides    
Mon Mar 17 Chapter 5 of the NLTK book.Taggers used on data. This NLTK-based tagging assignment is due next week. Tagging slides    
Mon Mar 24 Chapter 5 of the NLTK book.Taggers used on data. Computing Viterbi by hand. Tagging slides    
Mon Mar 31 H'day H'day H'day H'day H'day
Mon Apr 07 6.6 Maximum entropy models Max entropy assignment, Partial Viterbi answer. Lecture:: BBoard slides (Slp05.pdf)    
Mon Apr 14 6.6 Maximum entropy models. Background. Linear regression and logistic regression. 6.7-6.8 Max Ent tagging and Max Ent Markov Models.   Lecture    
Mon Apr 21 Chapter 12. Context Free Grammars of English, Treebanks Grammar assignment Parsing assignment topdown lecture Lecture: Top down parsing. Parsing as search.   td_parser-0.1:an implementation of a recursive descent top down recognizer.
Mon Apr 28 Chapter 13.1 Parsing. 13.4.1.2 CKY algorithm (bottum up parsing with a chart), Earley algorithm (top down parsing with chart) Assigment: Midterm, Parser writing help      
Mon May 05 Chapter 14. Probabilistic Context Free Grammars Assignment: Prob parsing assignment, CKy implementation in these notes Lecture    
Mon May 12 Chapter 19. Lexical Semantics. Chapter 20. Computational Lexical Semantics. Assignment: Lexical semantics assignment      
Mon May 19 Chapter 20. Computational Lexical Semantics. Assignment: Comp ling final