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 CSC 475/592 - Natural Language Processing

Course Syllabus

CSC 475/592:     Lecture: 

T     5:00pm-6:15pm  CI 1012

R     5:00pm-6:15pm  CI 1012

 

Schedule

INSTRUCTOR

Curry Guinn
E-mail: mailto:guinnc@uncw.edu?subject=CSC 475 (NLP)
Office hours:  MTWR 11:00am-12:00pm and by appointment
Phone: (910) 962-7937

COURSE DESCRIPTION

An introduction to natural language processing, computational linguistics, and speech recognition.   An exploration of both symbolic and stochastic algorithms for processing human languages.   Topics include parsing, part of speech tagging, semantic representation, pragmatic analysis, discourse structure, machine translation, n-gram statistical models, and Hidden Markov Models.  Prerequisite: CSC 332 and junior, senior, or graduate standing. 

Talk to our virtual assistant Sammi about 475. (Link removed)

TEXTBOOKS

Natural Language Processing with Python  by Bird, Klein & Roper, O’Reilly Media, 2009. (web available here: http://nltk.org/book/) 

The version that is compatible with Python 3.0 is here: http://nltk.org/book3/

. http://covers.oreilly.com/images/9780596516499/cat.gif

The Stuff of Thought: Language as a Window into Human Nature, by Steven Pinker, Penguin, 2008.  ISBN: 0143114247.

Description: Z:\courses\Spring12\475\CSC4753.jpg

GRADING

This course will require substantial problem-solving and programming outside of class.   Homeworks will involve a combination of "pencil-and-paper" problems as well as programming assignments.  

Course Learning Outcomes (CSC 475)

  1. Students can identify the fundamental syntactic structures of natural languages including constituency, grammatical relations, subcategorization, and dependency.
  2. Students can identify and explain the use of common symbolic data structures used in natural language processing such as regular and context-free grammars, finite state automata, unification grammars, and first-order logic.
  3. Students can identify and explain the user of common stochastic data structures used in natural language processing such as n-grams, hidden markov models, and Bayesian networks. 
  4. Students can implement solutions to natural language processing problems using both symbolic and stochastic data structures.
  5. Students develop skills in research, analysis, design, implementation, and presentation of natural language processing research questions.      

Course Learning Outcomes (CSC 592)

  1. Students can identify the fundamental syntactic structures of natural languages including constituency, grammatical relations, subcategorization, and dependency.
  2. Students can identify and explain the use of common symbolic data structures used in natural language processing such as regular and context-free grammars, finite state automata, unification grammars, and first-order logic.
  3. Students can identify and explain the user of common stochastic data structures used in natural language processing such as n-grams, hidden markov models, and Bayesian networks. 
  4. Students can implement solutions to natural language processing problems using both symbolic and stochastic data structures.
  5. Students develop skills in research, analysis, design, implementation, and presentation of natural language processing research questions.      
  6. Students demonstrate proficiency in hypothesis formation, experimental design, and the scientific method.
  7. Students demonstrate proficiency in written and oral scientific presentation. 

 

90 - 100 A

80 - 89 B

70 - 79 C

60 - 69 D

Honor Code

It is the responsibility of every student to follow the UNCW Academic Honor Code (see Section V of your Student Handbook). You violate the honor code when you represent someone else's work as your own. Individual programming assignments may be discussed at a conceptual (i.e. design and algorithms) level with other students but implementation details and coding must be your own. Team programming assignments must be completed without collaboration with other teams. Copying of programs is prohibited and will result in disciplinary action (see your Student Handbook). Copying includes digital copies, hand copies, as well as representing a slight modification of someone else's code as your own work.

Learning Strategies

You are expected to take an active role in your learning in this course. This includes regular attendance, paying attention in class, reading the textbook, and completing all course requirements. You are encouraged to study with your classmates outside of class. Programming assignments usually require a lot more time than expected, so start early and work some every day.

Students with Disabilities

If you have a disability and need reasonable accommodation in this course, you should inform the instructor of this fact in writing within the first week of class or as soon as possible. If you have not already done so, you must register with the Office of Disability Services in Westside Hall (extension 3746) and obtain a copy of your Accommodation Letter. You should then meet with your instructor to make mutually agreeable arrangements based on the recommendations of the Accommodation Letter.

Harassment Policy

UNCW practices a zero-tolerance policy for violence and harassment of any kind.  For emergencies contact UNCW CARE at 962-2273, Campus Police at 962-3184, or Wilmington Police at 911.  For University or community resources visit http://uncw.edu/wrc/crisis.htm.