Computational Linguistics Syllabus

Course Outline

Linguistics 581







Thu Jan 19 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. Polish fried fish. What is Computational Linguistics?  
Tue Jan 24 Chapter 2, 2.1-2.4 Finite-State Automata   Textbook Ch. 2 slides Non-deterministic automata, epsilon transitions    
Thu Jan 26 Reading: Sections 3.1-3.3 of Chapter 3. Introducing words and word parts. Sections 3.4-3.9 of Chapter 3. Assignment 2 Installing Python. Textbook Ch. 3 slides Introduction to transducers    
Tue Jan 31          
Thu Feb 02 Chapter 3. XFST intro. XFST assignment (Due Feb. 18) [Compling lab (if needed): SHW 243]      
Tue Feb 07          
Thu Feb 09 Chapter 4: J&M. 4.1-4.3. Word counting, frequency dictionaries, simple ngram models, the training corpus.   Ngrams Brief probability intro Introduction to NLTK. Peter Norvig on ngrams, word segmentation, spelling correction, statistical machine translation.    
Tue Feb 14          
Thu Feb 16 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. NLTK assignment, using Pylab, importing NLTK corpora. Lecture. Smoothing Lecture. Kneser-Ney Lecture.    
Tue Feb 21          
Thu Feb 23 5.1-5.4. Word-class and part of speech tagging. Rule-based taggers, 6.1, 6.2, 6.4 (decoding with HMMs). The Pollard assignment and smoothing assignment are due next week. Lecture. Tagging slides    
Tue Feb 28   Code example covered in class      
Thu Mar 02 Chapter 5 of the NLTK book.Taggers used on data. Tagging Assignment. Tagging slides    
Tue Mar 07          
Thu Mar 09 Chapter 5 of the NLTK book.Taggers used on data.   Tagging slides    
Tue Mar 14   Computing Viterbi by hand., Code for computing Brown tag counts. (discussied in class), Pollard Smoothing solution (in class code)      
Thu Mar 16 6.1-6.5 HMM Taggers/HMM models   Tokenization basics, Practical issues: tokenization, Viterbi in Python, Why Viterbi works, example Chapter 5 of the NLTK book.Taggers used on data, Manning on the state of the art of POS tagging, Manning 2011 paper on state of the art in POS tagging.    
Tue Mar 21   Answer to Viterbi tagging problem (use for midterm prep),      
Thu Mar 23 Chapter 12. Context Free Grammars of English, Treebanks Assignment: Compling midterm 2016. Jurafsky/ Martin parsing, topdown lecture, Lecture: Top down parsing,. Parsing as search.   td_parser-0.1:an implementation of a recursive descent top down recognizer.
Tue Mar 28 H'day H'day H'day H'day H'day
Thu Mar 30 H'day H'day H'day H'day H'day
Tue Apr 04          
Thu Apr 06 Chapter 13.1 Parsing. CKY algorithm (bottum up parsing with a chart), Earley algorithm (top down parsing with chart)        
Tue Apr 11   Grammar assignment Parsing assignment (CKY).      
Thu Apr 13 Chapter 14. Probabilistic Context Free Grammars   Lecture, Formal properties of PCFGs (for the mathematically inclined).    
Tue Apr 18   Assignment: Prob parsing assignment, CKY assignment solution, CKY implementation in these notes (not assigned).      
Thu Apr 20 Chapter 25. Machine Translation Prob parsing assignment solution. Machine translation notes on IBM alignment Model and EM.    
Tue Apr 25   Assignment: Machine translation assignment      
Thu Apr 27          
Tue May 02 Chapter 25 Machine translation continued. MT assignment solution, MT evaluation slides. Machine translation notes on IBM alignment Model and EM,    
Thu May 04   Final,   Last class day