Lab information for week 3:: To find out which lab group you are in, see the list of lab groups. If you are still not on any list, please go to the Tuesday group if possible, since it has a lot more extra capacity.
We will be using Piazza for course-related discussion. Please direct course-related questions to the forum, so other students can read, respond, and benefit from the answers.
The course provides a fast-paced introduction to the field of natural language processing, synthesizing research from linguistics and computer science. The course surveys theoretical and computational models of language, as well as the most important algorithms and data structures that are used to solve many NLP problems.
The course will cover formal models for representing and analyzing syntax and semantics of words, sentences, and discourse. Students will learn how to analyse sentences algorithmically, using hand-crafted and automatically induced treebank grammars, and how to build interpretable semantic representations. The course will also cover a number of standard models and algorithms that are used throughout NLP. Examples include Hidden Markov Models, the EM algorithm, and chart parsing.
This course assumes you are willing to engage with mathematical notation and concepts. Previous knowledge of probability theory is helpful but not required. However, if you do not have this background already, you will need to devote extra effort to learning it.
You should plan to work through the tutorials linked below during the first two weeks of class, and start now if you can! Once courses start you will only get busier. Even if you're not sure of taking this course, this background will be useful in many other Informatics courses too!
Labs and assignments use the Python programming language. We assume you either already know how to program, or are concurrently taking the Computer Programming for Speech and Language Processing course.
If you are already fluent in one or more programming languages, but not in Python, probably the best way to pick up what you need to know is by going through the official Python tutorial. Again, start sooner rather than later, as you'll only get busier.
Announcements regarding the course will be posted to the course mailing list. All students taking the course are automatically subscribed to this list. Previous postings can be accessed using the mailing list archive.
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