LING 681.02, Computational Linguistics aka Natural Language Processing

Time and place: MWF 3:00 - 3:50, NWCMB 202
Prof. Harry Howard
howard at tulane dot edu
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

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

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:

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 Powerpoint icon

26 (W)

2 Regular Expressions SLP 2.1 & Ex 2.1-.2; NLPP 1.1-.2 & Ex 1.8.1-.5 Powerpoint icon

28 (F)

3 NLTK   Powerpoint icon

31 (M)

4

NLPP §1.1 Computing with language
NLPP §1.2 A Closer Look at Python: Texts as Lists of Words

  Powerpoint icon

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

  Powerpoint icon

4 (F)

6

NLPP §1.3. Computing with Language: Simple Statistics
NLPP §1.4. Back to Python

Powerpoint icon Q1*
7 (M)
LABOR DAY

9 (W)

7 NLPP §2.1-.2   Powerpoint icon

11 (F)

8

NLPP §2.3-.5

  Powerpoint icon

14 (M)

9

NLPP §3.1

  Powerpoint icon

Q2

16 (W)

10

Strings & regular expressions

NLPP §3.2-4

Powerpoint icon

18 (F)

11

Regular expressions

NLPP §3.4-end

Powerpoint icon

21 (M)

12

Finite-state automata

SLP 2.1-2.2

Powerpoint icon

Q3

23 (W)

13

Finite-state automata

SLP 2.1-2.2

Powerpoint icon

25 (F)

14

Finite-state automata

Finish SLP §2, start SLP §3

Powerpoint icon

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 Powerpoint icon mp3 icon

Q9

11 (W)

32

Structured programming 2

NLPP 4

Powerpoint icon mp3 icon

13 (F)

33

Structured programming 3

NLPP 4

Powerpoint icon mp3 icon

16 (M)

34

Structured programming 4

NLPP 4

Powerpoint icon mp3 icon

Q10

18 (W)

35

Text classification

NLPP 6

Powerpoint icon mp3 icon

20 (F)

36

Extracting Information from Text 1

NLPP 7

Powerpoint icon mp3 icon

23 (M)

37

Extracting Information from Text 2

NLPP 7

Powerpoint icon mp3 icon

25 (W)

THANKSGIVING

27 (F)

THANKSGIVING

30 (M)

38

Semantics

NLPP 10 Powerpoint icon mp3 icon

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


Go back to Harry Howard's home page

Inception: 08/16/09. Last revision: December 1, 2009 . HH