Also, in the past some students in this class have used del.icio.us to tag Web pages that are related to this course -- see http://del.icio.us/tag/cmsc773. Please feel free to contribute!
The topics we'll cover are intended to get students up to speed on necessary background in order to understand and perform cutting-edge research in natural language processing, which requires a strong grounding in statistical NLP models and methods. Some of the topics are in the same areas as in Computational Linguistics I, but we will go deeper. As always, the syllabus is subject to revision; however, it will follow Manning and Schuetze's textbook relatively closely at least in early parts of the course.
As prerequisites, students are expected to be able to know how to program (your choice as to language), and will exercise this ability periodically in homework assignments and/or projects. Students are assumed to have taken the the first semester computational linguistics course or equivalent. (Although the content is not perfectly identical to CL1 as it was taught this year, this page links to PDF slides and video lectures from Fall 2013, if you need a refresher. An old classic that still has a few gems for corpus-processing, and also introduces a number of useful Unix command-line tools, is Ken Church's Unix for Poets.)
See the schedule of topics for class-by-class plans. In case of an emergency that closes the University for an extended period of time, see Piazza for announcements.
| 45% | Homework These are graded on a high-pass (100%), low-pass (50%) or fail (0%) basis. Some of these are one-week assignments, and others might be a multi-week assignment or project; either way the amount of time given for the assignment is calibrated to the amount of work that should be involved and the amount of credit you'll get for the assignment; for example, a particular homework might be described as a two-week assignment, meaning that you'll have two weeks to do it and you'll receive two homeworks' worth of credit for it. Assignments may involve on-paper exercises (e.g. walking through algorithms or calculations), hands-on programming, or analysis of data. I am comfortable in principle with students working together on assignments in part or in whole, but if you wish to do so, please ask me in advance because there are certain conditions that I will impose. | |
| 25% | Midterm exam This will be a take-home exam, and it will not involve programming. We often have a mixture of students, some of whom are able to work most on weekdays, others who really have most of their time on weekends; therefore I typically will hand out the exam at the end of class on Wednesday, and have it due at midnight on Sunday. But this does not mean that you're supposed to spend four full days working on the exam. If have mastered the content and are able to think critically about what we have covered in class, it shouldn't take any more time than typical take-home exams in other classes. I'm just giving you more wall-clock time for your flexibility. | |
| 25% | Final exam This will be structured as a multi-week team project that will definitely involve programming. | |
| 5% | Class participation I care enough about participation to make it part of the grade. It may be a small part, but it's definitely been known to tip the balance from a B+ grade to an A-, so please don't neglect it. This is necessarily subjective, because I am judging both the quantity and quality of your participation, but the calibration is pretty straightforward. It's a 5-point scale, and if you regularly ask relevant questions, volunteer answers (even if they're wrong!), and help make the class discussion interesting, you'll get 5 out of 5 points. If you show up to class prepared and contribute to the conversation every couple of classes, you'll get 3 out of 5 points. If you are regularly sitting in class but participating rarely or not at all, you'll get 1 point for showing up. If you don't show up consistently, you'll get zero. | |
| EC | Extra credit There will probably be some extra credit offered, either within assignments or as extra assignments. I will also offer extra credit, worth 25% of a homework, for attending the Computational Linguistics Colloquium and turning in a one-page writeup of the talk. (I'd prefer to post these on Piazza so the whole class can benefit, but it's ok if you'd prefer not to.) The summary must contain (a) a summary of what the talk was about, and (b) some discussion that shows you actually gave the talk some attention and thought, e.g. your opinions, how the talk relates to material in the class, possible applications of what you heard, etc. Writeups that just list what the speaker talked about ("The speaker talked about X. Then she talked about Y.") will receive no credit. If there are other talks for which you'd like to do this, e.g. NACS or Linguistics colloquium talks that are relevant for the class, ask me ahead of time and I'm happy to consider it, along with other possibilities if you have a legitimate schedule conflict that prevents your attending the CLIP Colloquium. There are eight or more talks per semester, and homeworks are 45% of your course grade, so attending these talks is a ridiculously easy way to boost your grade in the class. |
CS MS comps. MS comp grades for all students are the same as their final course grade. You really need to pay attention to this, because for CS there is a huge difference between a B+ and an A-.
I can tell you in advance that there are several common problems I will not consider as valid reasons for failing to get work in on time. These include (a) failure to manage your time properly, including being busy with another course, a piece of research, or a paper submission deadline; (b) discovering an assignment is harder than you expected it to be (see item a); and (c) losing code or data that should have been backed up, unless it's someone else's fault.
Cheating. What you represent as your own work must be your own work. However, talking with one another to understand the material better is strongly encouraged. Recognizing the distinction between cheating and cooperation is very important. If you simply copy someone else's solution, you are cheating. If you let someone else copy your solution, you are cheating. If someone dictates a solution to you, you are cheating. Everything you hand in must be in your own words, and based on your own understanding of the solution. If someone helps you understand the problem during a high-level discussion, you are not cheating. If you work collaboratively with explicit permission from the instructor, you are not cheating. We strongly encourage students to help one another understand the material presented in class, in the readings, and general issues relevant to the assignments. Any student who is caught cheating will be given an E in the course and referred to the University Student Behavior Committee. Please don't take that chance - if you're having trouble understanding the material, or if you need some help clarifying what is ok to do and what is not, please let us know and we will be more than happy to help.
Special needs. Any student eligible for and requesting reasonable academic accommodations due to a disability is requested to provide to the instructor a letter of accommodation from the Office of Disability Support Services (DSS) within the first two weeks of the semester. You may reach them at 301-314-7682 or by visiting Susquehanna Hall on the 4th Floor.
Mental health issues. Let's face it: grad school can be really hard. Sometimes students don't know that they need help, or they somehow know they're in trouble but they don't know what to do about it. What's really important for you to know is that at a big university like this one, you don't need to cope with it alone. There are many people on this campus who know how to help students in all kinds of circumstances. It's their job. You can find detailed information about services at beta.umd.edu.
Anti-Harassment. The open exchange of ideas, the freedom of thought and expression, and respectful scientific debate are central to the aims and goals of a this course. These require a community and an environment that recognizes the inherent worth of every person and group, that fosters dignity, understanding, and mutual respect, and that embraces diversity. Harassment and hostile behavior are unwelcome in any part of this course. This includes: speech or behavior that intimidates, creates discomfort, or interferes with a person’s participation or opportunity for participation in the conference. We aim for this course to be an environment where harassment in any form does not happen, including but not limited to: harassment based on race, gender, religion, age, color, national origin, ancestry, disability, sexual orientation, or gender identity. Harassment includes degrading verbal comments, deliberate intimidation, stalking, harassing photography or recording, inappropriate physical contact, and unwelcome sexual attention. Please contact an instructor or staff member if you have questions or if you feel you are the victim of harassment (or otherwise witness harassment of others), or see beta.umd.edu for pointers to relevant resources.
Philip Resnik, Professor
Department of Linguistics and Institute for Advanced Computer Studies
Department of Linguistics
1401 Marie Mount Hall UMIACS phone: (301) 405-6760
University of Maryland Linguistics phone: (301) 405-8903
College Park, MD 20742 USA Fax: (301) 314-2644 / (301) 405-7104
http://umiacs.umd.edu/~resnik E-mail: resnik AT umd _DOT.GOES.HERE_ edu