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. A number 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.
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.
I assign final course grades by sorting everyone's total grades, identifying gaps between groups of students, and assigning students within those gaps the same grade. Creating equivalence classes in this way helps to ensure that students who are only a small fraction apart get the same grade. (For example, if three students have course totals of 86.1%, 89.9%, and 90.1%, I believe that its logical and fair for the middle student to be grouped with the student above rather than the student below.) Although there's no guarantee, allowing for that adjustment, letter grades often tend to work out reasonably close to the usual numeric ranges (90+ A, 80+ B, etc.) with plus and minus grades introduced as needed to permit appropriately finer-grained equivalence classes. Components of the total grade are as follows:
| 45% | Homework These are graded on a coarse 5-point scale, roughly corresponding to great (you totally nailed it, and probably went above and beyond what's required); good (you did everything that's required really well); pass (you did a solid job on everything that's required, mostly well); low pass (there are some parts of the assignment you really didn't seem to get); and fail (you may have done ok on some component of the assignment, but we don't feel like you demonstrated enough mastery of the material to consider the assignment complete). Typically students earn good or pass, although we love to see assignments that earn great. 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. In a typical semester there are five or six assignments, mostly during the first half of the semester. Usually the second half of the semester, after the midterm, is focused on the final exam project. I am comfortable with students working together on assignments in part or in whole, and in fact I encourage it; if you'd like to do that please talk with me in advance so that we can discuss how to make sure you stay on the right side of the university's policies on academic dishonesty. | |
| 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 during the weekend. But this does not mean that you're supposed to spend all that time working on the exam. If you 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 significant, long-term team project that will definitely involve programming. It typically involves an open research problem that I will give you. Grading is based on the writeup, so it is extremely important that you devote significant time and attention to quality when writing up the project; don't leave the writing to the last minute. | |
| 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. Participation in class and on Piazza both count. This is necessarily subjective, because I am judging both the quantity and quality of your participation, but the calibration is pretty straightforward. Things that push toward the top of the 5-point scale include and very regularly asking relevant questions, volunteering answers (even if they're wrong!), and helping make the class discussion interesting. If you show up to class prepared and contribute to the conversation every couple of classes, you'll typically get 3 out of 5 points. If you are regularly sitting in class but participating rarely or not at all, you might get 1 point for showing up. If you don't show up consistently, you'll get zero. | |
| (4%) | Extra credit for attending talks You can earn 0.25% extra credit, up to a cumulative cap of 4%, for attending a relevant talk and turning in a one-page discussion of the talk by posting it on Piazza. (As a rule of thumb, one page is typically 400-500 words.) Attending the Computational Linguistics Colloquium definitely qualifies, and if there are other talks for which you'd like to do this, e.g. NACS or Linguistics speakers 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.
Getting credit for talks requires not only showing that you were there and actually listening to the talk, but also discussing what you heard in a thoughtful way, e.g. offering a thoughtful opinion (critiques are encouraged!), identifying pros and cons, relating what you heard to things we covered in class, etc. Specifically, the discussion should contain two clearly identified sections: (A) Going to relevant talks is a ridiculously easy way to boost your grade and see what's currently going on in the field, so I really encourage it. | |
| EC | Extra credit assignments There will also probably be some extra credit offered. Typically a couple of assignments include some extra credit to boost your grade by up to 10%, and sometimes there might be a whole extra credit assignment worth 50% or even 100% of a homework. |
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-. Did I mention that class participation can be really important, even though it's only a small percentage of the grading formula?
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 clearly 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 F 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. Some resources you can take advantage of include the Counseling Center, in the Shoemaker Building, 301-314-7651, and Mental Health Services, in the Health Center, 301-314-8106; the Office of Student Affairs, 301-314-8430, is another place you can connect with to find help of various kinds.
If you are concerned about the behavior of another student, and in particular if you are worried that they might pose a threat to themselves or others, see this page for students concerned about another student.
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 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.
Religious holidays. please send the TA (email above, cc the instructor) a list of all holidays you observe during the semester by the end of the first week of classes, so they can be taken into account in the course schedule.
Course evaluations. We welcome your suggestions for improving this class, please don’t hesitate to share your thoughts with the instructor or the TA during the semester! You will also be asked to give feedback using the CourseEvalUM system at the end of the semester. Your feedback will help us make the course better.
Right to change information. Although every effort has been made to be complete and accurate, unforeseen circumstances arising during the semester could require the adjustment of any material given here. Consequently, given due notice to students, the instructor reserves the right to change any information on this syllabus or in other course materials. If you have concerns about any changes please discuss them with the instructor.
Philip Resnik, Professor
Department of Linguistics and Institute for Advanced Computer Studies
Department of Linguistics
1401 Marie Mount Hall
University of Maryland Linguistics phone: (301) 405-7002
College Park, MD 20742 USA Linguistics fax: (301) 405-7104
http://umiacs.umd.edu/~resnik E-mail: resnik AT umd _DOT.GOES.HERE_ edu