In Fall 2013, this course is slated to be renumbered as CSCI 662 and renamed Advanced Natural Language Processing. Please visit the new course page for more information.
Computers process massive quantities of information every day in the form of human language, yet machine understanding of human language remains one of the great challenges of computer science. How can advances in computing technology enable more intelligent processing of all this language data? Will computers ever be able to use this data to learn language like humans do? This course provides a systematic introduction to statistical models of human language, with particular attention to the structures of human language that inform them and the structured learning and inference algorithms that drive them. This is a lecture course, not a seminar course, but aims to cover both fundamental and cutting-edge research issues.
Audience This graduate course is intended for:
Prerequisites Students are expected to be proficient in programming, basic algorithms and data structures, discrete math, and basic probability theory. Undergraduate students are welcome as well. If you are not sure if you have enough background for this course, you are welcome to sit in on the first few lectures. There will be a Quiz 0 to test if you qualify for this course.
Related Courses This course is part of USC's curriculum in natural language processing. There is a sister course, Natural Language Processing (CSCI 544), offered in the Spring semester. You can take these two in either order.
CourseworkStudents will experiment with existing NLP software toolkits and write their own programs. Grades will be based on:
All assignments and the project will be due at the beginning of class (11am) on the due date. Late assignments will be accepted with a 30% penalty up to one week late. No exceptions can be made except for a grave reason.
Students are expected to submit only their own work for homework assignments. They may discuss the assignments with one another but may not collaborate with or copy from one another.
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.
|Tue, Aug 28||What is natural language processing? What is the empirical approach to NLP?||Knight||HW0: nothing to turn in.|
|Thu, Aug 30||Introduction to language.||Chiang||Quiz 0|
|Unit One: Sequence Models (10 lectures)|
|Tue, Sep 4||Basic automata theory. Finite-state acceptors and transducers.||Knight|
|Thu, Sep 6||HW1 out|
|Tue, Sep 11||Probability theory and estimation. Weighted FSTs. The noisy-channel model.|
|Thu, Sep 13
|Tue, Sep 18||Language models and their applications.
Learning models from data.
|Thu, Sep 20||HW2 out|
|Tue, Sep 25||TBD|
|Thu, Sep 27||String transformations and their applications.|
|Tue, Oct 2||Knight||HW2 due|
|Thu, Oct 4||Quiz 1|
|Unit Two: Learning Language (8 lectures)|
|Tue, Oct 9||Unsupervised training. Expectation-Maximization. The Forward-Backward algorithm.||Knight|
|Thu, Oct 11||HW3 out|
|Tue, Oct 16|
|Thu, Oct 18|
|Tue, Oct 23||Discriminative training. The perceptron algorithm. Conditional random fields. Max-margin training.||Chiang|
|Thu, Oct 25||HW3 due
|Tue, Oct 30||Quiz 2|
|Thu, Nov 1||Bayesian inference.||Knight|
|Unit Three: Tree Models (7 lectures)|
|Tue, Nov 6||Context-free grammars (CFGs). Parsing. Inside-Outside algorithm.||Chiang||HW4 due|
|Thu, Nov 8|
|Tue, Nov 13||TBD||Initial project proposal due|
|Thu, Nov 15
|Statistical parsing.||Chiang||HW 5 out|
|Tue, Nov 20||Final project proposal due|
|Thu, Nov 22||Thanksgiving – no class|
|Tue, Nov 27||Synchronous CFGs and machine translation.||Chiang||HW5 due|
|Thu, Nov 29||Quiz 3|
|Tue, Dec 4||Project interim presentations||You|
|Thu, Dec 6|
|Wed, Dec 19||Final projects due|
|[LY90]||The estimation of stochastic context-free grammars using the inside-outside algorithm.|
|K. Lari and S. J. Young.|
|Computer Speech and Language, 1990: 4:35-56.|
|[SSP95]||Principles and Implementation of Deductive Parsing.|
|Stuart Shieber, Yves Schabes, and Fernando Pereira.|
|Journal of Logic Programming, 1995: 24 (1-2): 3-36.|
|[H08]||Tutorial on Semirings and Advanced Dynamic Programming in NLP (slides).|
|Tutorial given at COLING 2008 and NAACL 2009.|
|[KS09]||Tutorial on Writing Systems, Transliteration, and Decipherment (slides).|
|Kevin Knight and Richard Sproat|
|Tutorial given at NAACL 2009.|
|[CG98]||An Empirical Study of Smoothing Techniques for Language Modeling|
|Stanley F. Chen and Joshua Goodman|
|Technical Report TR 10-98, Computer Science Group, Harvard University.||[DLR77]||Maximum Likelihood from Incomplete Data via the EM Algorithm|
|A.P. Dempster, N.M. Laird, and D.B. Rubin|
|Journal of the Royal Statistical Society, Vol. 39, No. 1, 1977|
|[C97]||The EM Algorithm|
|[D02]||The Expectation Maximization Algorithm|
|Technical Report, 2002|
|[B06]||The Expectation Maximization Algorithm -- A short Tutorial.|