CSCI 544

Applied Natural Language Processing

University of Southern California

Spring 2013

Time:

Location:

Instructor:

Zornitsa Kozareva

Teaching Assistant:

Victor Barres

Guest Lecturers:

Kenji Sagae, ICT

Sujith Ravi, Google

Anton Leuski, ICT

CSCI544-2013 Final Project Award Winners

Class Questions:

Goals:

Audience:

• students who want to understand state-of-the-art and current NLP research
• students interested in tools for building NLP applications
• students interested in applications of NLP like sentiment analysis, information extractors, search engines among others

Prerequisities:

Related Courses

Textbooks (optional reading)

• Daniel Jurafsky and James Martin. Speech and Language Processing, 2nd edi., Prentice Hall, 2008.
• Christopher Manning and Hinrich Schütze. Foundations of Statistical Natural Language Processing, MIT Press, 1999.
• James Allen. Natural Language Understanding (2nd ed), Addison Wesley, 1994.
• Ian H. Witten and Eibe Frank. Data Mining: Practical Machine Learning Tools and Techniques (3rd ed), Morgan Kaufmann, 2005.
• Steven Bird, Ewan Klein, and Edward Loper. Natural Language Processing with Python - Analyzing Text with the Natural Language Toolkit, O'Reilly Media, 2009.

Classes from Previous Years

Syllabi and materials from previous years. Since those pages are no longer maintained, there is no guarantee of completeness.

• Spring 2012
• Spring 2011
• Spring 2010

Coursework:

• Programming assignments (2 x 25%): the grade will depend on the performance of a system relative to the rest of the class and the technical report.
• Research project (50%): the grade will depend on the project's substantiality, correctness, relevance to the course, as well as the clarity and depth of the project report, which should follow standard ACL guidelines. Building a demo system will be optional, but will count as bonus points.

Homeworks and Project Proposal Guidelines

Syllabus

Statement for Students with Disabilities:

Any student requesting academic accommodations based on a disability is required to register with Disability Services and Programs (DSP) each semester. A letter of verification for approved accommodations can be obtained from DSP. Please be sure the letter is delivered to me (or to TA) as early in the semester as possible. DSP is located in STU 301 and is open 8:30 a.m.-5:00 p.m., Monday through Friday. The phone number for DSP is (213) 740-0776.

Statement on Academic Integrity:

USC seeks to maintain an optimal learning environment. General principles of academic honesty include the concept of respect for the intellectual property of others, the expectation that individual work will be submitted unless otherwise allowed by an instructor, and the obligations both to protect one's own academic work from misuse by others as well as to avoid using another's work as one's own. All students are expected to understand and abide by these principles. Scampus, the Student Guidebook, contains the Student Conduct Code in Section 11.00, while the recommended sanctions are located in Appendix A: http://www.usc.edu/dept/publications/SCAMPUS/gov/. Students will be referred to the Office of Student Judicial Affairs and Community Standards for further review, should there be any suspicion of academic dishonesty. The Review process can be found at: http://www.usc.edu/student-affairs/SJACS/.

Emergency Preparedness/Course Continuity in a Crisis:

In case of a declared emergency if travel to campus is not feasible, USC executive leadership will announce an electronic way for instructors to teach students in their residence halls or homes using a combination of Blackboard, teleconferencing, and other technologies.