This course is designed to introduce students to the fundamental concepts and ideas in natural language processing (NLP), and to get them up to speed with current research in the area. It develops an in-depth understanding of both the algorithms available for the processing of linguistic information and the underlying computational properties of natural languages. Word-level, syntactic, and semantic processing from both a linguistic and an algorithmic perspective are considered. The focus is on modern quantitative techniques in NLP: using large corpora, statistical models for acquisition, disambiguation, and parsing. Also, it examines and constructs representative systems.
Graduate students and advanced undergraduates specializing in computer science, linguistics, or symbolic systems.
Textbook and Readings
The most used book will be:
It's referred to as M&S below. Please see http://nlp.stanford.edu/fsnlp/ for supplementary information about the text, including errata, and pointers to online resources.
Other useful reference texts for NLP are:
Papers will occasionally be distributed and discussed during the course of the class.
Copies of in-class hand-outs, such as readings and programming assignments, will be posted on the syllabus, and hard copies will also be available outside Gates 158 (in front of Prof. Manning's office) while supplies last.
Assignments and Grading
There will be three substantial programming assignments, each exploring a core NLP task.
In addition, there will be a final programming project on a topic of your own choosing.
Course grades will be based 60% on programming assignments (20% each) and 40% on the final project.
Sections will be held most weeks to go over background material, or to address issues related to the programming assignments. Sections are optional, but students are encouraged to attend for a better understanding of background material and the assignments.
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