LING 681.02, Computational Linguistics aka Natural Language Processing

Time and place: MWF 3:00 - 3:50, NWCMB 202
Prof. Harry Howard

Office: Newcomb Hall 322-D
862-3417 (voice mail 24 hours a day)
Office hours: T 3-5, W 4-5

Objectives: Computational Linguistic shows you how to make a computer perform various useful tasks with natural language. Through it you will learn

• some linguistics,
• some algorithms,
• some statistics,
• and some computer programming in Python.

Outcomes: For you to demonstrate your understanding of these objectives, you will perform the following tasks:

• Take a quiz or turn in a project almost every week, usually on Monday. [11-1 * 7.5% = 75%]
  • No quiz/project can be accepted late, though I will drop the lowest grade.
  • Even though these look like a lot of small grades, missing just one lowers your final grade almost an entire letter, as an unfortunate few have found out the hard way.
  • If you know ahead of time that you will miss a quiz/project, send me an e-mail beforehand, and I will excuse you with no penalty.
• Present a final project to the class on the final exam day (Dec 14) and turn in a report of your project within two days. [25%]
  • This may be a group effort, but the entire group will receive the same grade.
• Note that there is no credit for class participation, but I will change any high Y- into a low X+ if I notice you participating in class.

Code of Academic Integrity

“The integrity of Newcomb-Tulane College is based on the absolute honesty of the entire community in all academic endeavors. As part of the Tulane University community, students have certain responsibilities regarding work that forms the basis for the evaluation of their academic achievement. Students are expected to be familiar with these responsibilities at all times. No member of the university community should tolerate any form of academic dishonesty, because the scholarly community of the university depends on the willingness of both instructors and students to uphold the Code of Academic Conduct. When a violation of the Code of Academic Conduct is observed it is the duty of every member of the academic community who has evidence of the violation to take action. Students should take steps to uphold the code by reporting any suspected offense to the instructor or the associate dean of the college. Students should under no circumstances tolerate any form of academic dishonesty.” For further information, point your browser at http://college.tulane.edu/honorcode.htm.

Violations of the Code of Academic Integrity will not be tolerated in this class. I will rigorously investigate and pursue any such transgression.

Students with disabilities who need academic accommodation should:

• Contact and register with the Office of Disability Services (ODS). For more information, visit the ODS website at http://erc.tulane.edu/disability/.
• Bring official notice to me from the ODS indicating that you need academic accommodation. This should be done before the first quiz/project.

Schedule of assigments, Fall 2009
Textbooks
Speech and Language Processing, 2e, (2008) by Daniel Jurafsky and James H. Martin [SLP]
Natural Language Processing with Python, 1e, (2009) by Steven Bird, Ewan Klein, and Edward Loper [NLPP]
There may be additional readings assigned from other sources.

Date	Day	Topic	Assignment	ppt	mp3	Q/P
Aug 24 (M)	1	Introduction to the course	SLP 1; NLPP Preface
26 (W)	2	Regular Expressions	SLP 2.1 & Ex 2.1-.2; NLPP 1.1-.2 & Ex 1.8.1-.5
28 (F)	3	NLTK
31 (M)	4	NLPP §1.1 Computing with language NLPP §1.2 A Closer Look at Python: Texts as Lists of Words
Sept 2 (W)	5	NLPP §1.2 A Closer Look at Python: Texts as Lists of Words NLPP §1.3. Computing with Language: Simple Statistics
4 (F)	6	NLPP §1.3. Computing with Language: Simple Statistics NLPP §1.4. Back to Python				Q1*
7 (M)		LABOR DAY
9 (W)	7	NLPP §2.1-.2
11 (F)	8	NLPP §2.3-.5
14 (M)	9	NLPP §3.1				Q2
16 (W)	10	Strings & regular expressions	NLPP §3.2-4
18 (F)	11	Regular expressions	NLPP §3.4-end
21 (M)	12	Finite-state automata	SLP 2.1-2.2			Q3
23 (W)	13	Finite-state automata	SLP 2.1-2.2
25 (F)	14	Finite-state automata	Finish SLP §2, start SLP §3
28 (M)		YOM KIPPUR
30 (W)	15	Morphology 1				Q4*
Oct 2 (F)	16	Morphology 2
5 (M)	17	AWAY
7 (W)	18	AWAY
9 (F)	19	Minimum edit distance
12 (M)	20	Words and Transducers N-grams, Part-of-Speech Tagging, Hidden Markov and Maximum Entropy Models, Formal Grammars of English, Syntactic Parsing, Syntactic Parsing, Statistical Parsing, Features and Unification	SLP 4, 5, 6, 12 13, 14 15			Q5
14 (W)	21	Features and Unification	SLP 15
16 (F)		FALL BREAK
19 (M)	22	Language and Complexity	SLP 16			Q6
21 (W)	23	Language and Complexity	SLP 16
23 (F)	24	The Representation of Meaning	SLP 17
26 (M)	25	The Representation of Meaning	SLP 17			Q7
28 (W)	26	Computational Semantics	SLP 18
30 (F)	27	Computational Semantics	SLP 18
Nov  2 (M)	28	Lexical Semantics	SLP 19			Q8
4 (W)	29	Lexical Semantics	SLP 19
6 (F)	30	Computational Lexical Semantics	SLP 20
9 (M)	31	Structured programming	NLPP 4			Q9
11 (W)	32	Structured programming 2	NLPP 4
13 (F)	33	Structured programming 3	NLPP 4
16 (M)	34	Structured programming 4	NLPP 4			Q10
18 (W)	35	Text classification	NLPP 6
20 (F)	36	Extracting Information from Text 1	NLPP 7
23 (M)	37	Extracting Information from Text 2	NLPP 7
25 (W)		THANKSGIVING
27 (F)		THANKSGIVING
30 (M)	38	Semantics	NLPP 10
Dec 2 (W)	39	Semantics 2	SLP 24			Q11*
4 (F)	40	Machine Translation	SLP 25
14 (M)	41	FINAL EXAM DAY 1-5 pm

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