CMSC 723: Computational Linguistics I (Fall 2009): Course Information

What's the course about?

This is the first semester in our two-semester graduate sequence in computational linguistics. In the first semester, we will be discussing fundamental methods in natural language processing, with a focus on core representations and algorithms. Topics include: finite-state methods, context-free and extended context-free models of syntax; parsing and semantic interpretation; n-gram and Hidden Markov models, part-of-speech tagging; word sense disambiguation, semantic distance; and natural language applications. Students completing this course will have a solid working knowledge of the basics of NLP and will be well prepared for the second semester course, which covers natural language processing with a focus on corpus-based statistical techniques. See the syllabus for more details of what will be covered.

The textbook we're using is Speech and Language Processing (2nd edition, 2008), by Jurafsky and Martin.

Pre-requisites

Students taking this course should be competent programmers, since concepts taught in class will sometimes be reinforced in practice by hands-on programming assignments. The course will be taught in Python with the Natural Language Toolkit (NLTK). We are not assuming a great deal of familiarity with linguistics; students needing to get up to speed on linguistics concepts will do fine with Jurafsky and Martin's material on word classes and context-free grammars for English (Chapters 5 and 12).

Course Grade

• Exams (50%): There will be a midterm exam (25%) and a final exam (25%). CS MS comp grade (AI area) will be based entirely on the average of the two exams.
• Class assignments (45%): There will be five homework assignments. Assignment 1 is considered a "warm-up" exercise and is worth 5% of your grade. The rest of the assignments are each worth 10% of your grade.
• Class participation (5%): Showing up for class, demonstrating preparedness (i.e., doing the readings), and contributing to class discussions.

See syllabus for assignment due dates and exams dates.

Policy for Late and Incomplete Work

• Assignment deadlines: Assignments are due at the beginning of class unless otherwise noted.
• Late assignments: If an assignment is late by up to 24 hours, the grade will be reduced by 20%. By 48 hours, 40%. And so forth. Exceptions can be discussed in cases of medical excuses, family emergencies, etc., but being busy is not a valid excuse. Exceptions will only be considered prior to the deadline, and the sooner you talk to me about a problem the more likely I will be sympathetic. There are several common problems that I am unlikely to consider as valid reasons for failing to get work in on time. These include (a) failure to manage your time properly, (b) discovering an assignment is harder or takes longer 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.
• 'Incomplete' as a grade: I will not issue an 'incomplete' as a grade except for serious, valid reasons, generally in the category of emergencies. See above for some reasons unlikely to be considered valid. If you are having problems of any kind, please talk to me as soon as possible.

Course Software

You are expected to have a computer for this course, which will be taught with Python and NLTK. Information about the software, including download instructions, is found on this page.

Academic Integrity

The University of Maryland, College Park has a nationally recognized Code of Academic Integrity, administered by the Student Honor Council. This Code sets standards for academic integrity at Maryland for all undergraduate and graduate students. As a student you are responsible for upholding these standards for this course. It is very important for you to be aware of the consequences of cheating, fabrication, facilitation, and plagiarism. Please visit the Code of Academic Integrity or the Student Honor Council for more information.

Students with Disabilities

Students with disabilities needing academic accommodation should: (1) register with and provide documentation to the Disability Support Services office, and (2) discuss any necessary academic accommodation with me. This should be done at the beginning of the semester, within the first three class sessions.

Emergency Preparedness

Information about the status of the campus is available at the campus emergency preparedness website. If the campus is closed, please make sure to stay safe. Information about possible rescheduling of course activities will be provided via e-mail once the campus has reopened.

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This page, first created: 03 Aug 2009; last updated: