Instructor: David Smith, Assistant Professor in Computer and Information Science (Office Hours: Thursdays, 3-5; WVH 356)
TA: Moonyoug Kang (Office Hours: Tuesdays, 3-5; WVH 472)
Class meeting: Thursdays, 6-9 p.m., Hayden 221
This is a graduate course that introduces you to natural language processing; it is also an introduction to reading papers in natural language processing. In addition to reading and discussing papers from the NLP literature, you will, in the latter part of the course, write a review of the literature and open problems in an area of NLP. Fortunately, the NLP community has a robust tradition of open-access publication, primarily via the Association for Computational Linguistics' ACL Anthology.
Along with these readings, lectures will provide background in the fundamental linguistics concepts, statistical models, and algorithms used in NLP. These lectures will primarily draw on material from two textbooks which, while not required, provide more useful information:
Lecture notes and readings will be posted on the syllabus.
You will read, on average, one paper a week. The goal is not necessarily to figure out every detail of that one paper but rather to understand how each paper fits with what you've learned about NLP as a whole and what future questions it suggests. In other words, the process should mimic what you would do when conducting research in NLP or other areas of applied CS. Near the end of class, you will also give a short presentation on your literature review (see below). This presentation will also count towards the participation score, which totals 20% of the course grade.
There will be four homework assignments for 40% of the total course grade. Assignments will mix written derivations and explanations with some programming problems. If you work with others on a problem, you must note with whom you discussed the problem at the beginning of your solution write-up.
In the latter part of the course, you will write a review of the literature in an area of NLP, which will constitute 40% of the course grade. First, you will consult with the instructor about an appropriate scope for the review. For instance, “parsing” or “machine translation” or “semantics” are far too broad. Then, you will hand in a first draft that is essentially an annotated bibliography, with roughly a paragraph about how each paper relates to the general topic. Finally, you will hand in the full review and give a short presentation on it (see above). The review should not be structured like an annotated bibliography, where each paper is simply discussed in turn; rather, you should structure the discussion around larger themes.