CMSC 723: Computational Linguistics I (Fall 2009)

University of Maryland, College Park

CMSC 723: Computational Linguistics I (Fall 2009): Syllabus

Note: All readings are to be completed before class. J&M refers to the Jurafsky and Martin textbook.

#	Date	Topic	Assignment Due	Details
1	9/2	Introduction to NLP		[show]
1	9/2	Introduction to NLP Readings (complete before class) Chapter 1 of J&M: "Introduction" Chapter 1 of Foundations of Statistical Natural Language Processing Topics Administrivia Semester plan Overview of NLP Overview of statistical NLP Material Lecture slides: PowerPoint (1.46 MB), PDF (467 KB)		[Hide]
2	9/9	Nuts and Bolt		[show]
2	9/9	Nuts and Bolt Readings (complete before class) NLTK Book (Chapters 1 and 2) Python Beginners' Guide (includes resources for both programmers and non-programmers) ACM Crossroads article on Getting Started on NLP with Python. Topics Introduction to Python and NLTK Material Lecture slides: PDF (3.9 MB) Data to "name that bill!" exercise: 1, 2		[Hide]
3	9/16	Words: finite-state morphology	Assignment #1	[show]
3	9/16	Words: finite-state morphology Assignment #1 due at the beginning of class. Readings (complete before class) Chapter 2 of J&M: "Regular Expressions and Automata" Chapter 3 of J&M: "Words and Transducers" Topics Regular expressions Finite state automata Finite state transducers Overview of morphological processes Computational morphology Material Lecture slides: PowerPoint (1.98 MB), PDF (1.96 KB)		[Hide]
4	9/23	Words: part-of-speech tagging		[show]
4	9/23	Words: part-of-speech tagging Readings (complete before class) Chapter 5 (Sections 5.1-5.4 and 5.6) of J&M: "Part-of-Speech tagging" Topics Parts of speech Part-of-speech tagging Rule-based approaches Transformation-based learning Material Lecture slides: PowerPoint (924 KB), PDF (1.05 MB)		[Hide]
5	9/30	Words: hidden Markov models (HMMs)	Assignment #2	[show]
5	9/30	Words: hidden Markov models (HMMs) Assignment #2 due at the beginning of class. Readings (complete before class) Chapter 5 (Section 5.5) of J&M: "Part-of-Speech tagging" Chapter 6 (Sections 6.1-6.5) of J&M: "Hidden Markov and Maximum Entropy Models" Topics Hidden Markov models (HMMs) Viterbi decoding Expectation-maximization and HMM training Material Lecture slides (with solutions): PowerPoint (582 KB), PDF (636 KB) Handout: PowerPoint (155 KB) Note: ufortunately, PDF exports of these slides are not readable due to the quality of some of the images; please print directly from PowerPoint sources.		[Hide]
6	10/7	Structure: syntax and context-free grammars		[show]
6	10/7	Structure: syntax and context-free grammars Readings (complete before class) Chapter 12 of J&M: "Formal Grammars of English" Topics Context-free grammar (CFG) Grammars for English Treebanks Dependency grammars Material Lecture slides: PowerPoint (407 KB), PDF (344 KB)		[Hide]
7	10/14	Structure: syntactic parsing with CFGs	Assignment #3	[show]
7	10/14	Structure: syntactic parsing with CFGs Assignment #3 due at the beginning of class. Readings (complete before class) Chapter 13 of J&M: "Syntactic Parsing" Topics Top-down and bottom-up parsing CKY parsing Earley parsing Material Lecture slides: PowerPoint (1.36 MB), PDF (1.08 MB)		[Hide]
8	10/21	Midterm
8	10/21			[Hide]
9	10/28	Structure (sort of): n-gram language models		[show]
9	10/28	Structure (sort of): n-gram language models Readings (complete before class) Chapter 4 (Sections 4.1-4.7; 4.9.1) of J&M: "N-Grams" Topics N-gram language models Material Lecture slides: PowerPoint (523 KB), PDF (489 KB)		[Hide]
10	11/4	Meaning: semantic similarity	Assignment #4	[show]
10	11/4	Meaning: semantic similarity Assignment #4 due at the beginning of class. Readings (complete before class) Chapter 19 of J&M: "Lexical Semantics" Chapter 20 (Sections 20.1-20.5) of J&M: "Computational Lexical Semantics" Topics Lexical semantic relations WordNet Semantic similarity/distance Material Lecture slides: PowerPoint (4.70 MB), PDF (805 KB)		[Hide]
11	11/11	Meaning: word sense disambiguation		[show]
11	11/11	Meaning: word sense disambiguation Readings (complete before class) Chapter 20 (Sections 20.6-20.9) of J&M: "Computational Lexical Semantics" Topics Word sense disambiguation Shallow semantics Material Lecture slides: PowerPoint (2.25 MB), PDF (1.38 MB)		[Hide]
12	11/18	MapReduce and Data Intensive NLP	Assignment #5	[show]
12	11/18	MapReduce and Data Intensive NLP Assignment #5 due on 11/23. Readings (complete before class) Dean, Jeffrey and Sanjay Ghemawat. (2004) MapReduce: Simplified Data Processing on Large Clusters. Proceedings of the 6th Symposium on Operating System Design and Implementation (OSDI 2004). Brants, Thorsten, Ashok C. Popat, Peng Xu, Franz J. Och, and Jeffrey Dean. (2007) Large Language Models in Machine Translation. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning. Topics Introduction to MapReduce MapReduce algorithm design Language modeling with MapReduce Material Lecture slides: PowerPoint (2.27 MB), PDF (2.08 MB)		[Hide]
13	12/2	Applications 1: Information Retrieval		[show]
13	12/2	Applications 1: Information Retrieval Guest lecture by Ken Church Readings (complete before class) S. Deerwester, S. Dumais, G. W. Furnas, T. K. Landauer, R. Harshman. (1990) Indexing by Latent Semantic Analysis. Journal of the American Society for Information Science, 41(6):391–407. (Optional) Chapters 18-21 of http://nlp.stanford.edu/IR-book/information-retrieval-book.html Topics Information retrieval Latent-Semantic indexing Web search Material Lecture slides: PowerPoint (11.4 MB), PDF (3.46 MB)		[Hide]
14	12/9	Applications 2: Recognition, Transduction, etc.		[show]
14	12/9	Applications 2: Recognition, Transduction, etc. Readings (complete before class) Kenneth W. Church and Robert L. Mercer. (1993) Introduction to the Special Issue on Computational Linguistics Using Large Corpora. Computational Linguistics, 19(1):1-24. Topics Recognition, transduction, etc. Noisy channel model Material Lecture slides: PowerPoint (7.31 MB), PDF (2.91 MB)		[Hide]
15	12/16	Final Exam

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This page, first created: 03 Aug 2009; last updated:

CMSC 723: Computational Linguistics I (Fall 2009): Syllabus

Introduction to NLP

Nuts and Bolt

Words: finite-state morphology

Words: part-of-speech tagging

Words: hidden Markov models (HMMs)

Structure: syntax and context-free grammars

Structure: syntactic parsing with CFGs

Structure (sort of): n-gram language models

Meaning: semantic similarity

Meaning: word sense disambiguation

MapReduce and Data Intensive NLP

Applications 1: Information Retrieval

Applications 2: Recognition, Transduction, etc.