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Instructor Class Description

Time Schedule:

Gina-Anne Levow
LING 571
Seattle Campus

Deep Processing Techniques for Natural Language Processing

Algorithms for associating deep or elaborated linguistic structures with naturally occurring linguistic data (parsing/semantics/discourse), and to produce natural language strings from input semantic representations (generation). Prerequisite: a minimum grade of 2.7 in each of CSE 326 or equivalent, STAT 391 or equivalent, and LING 473 or passing score on the placement exam. Offered: A.

Class description

This course covers algorithms for associating deep or elaborated linguistic structures with naturally occurring data, covering parsing, semantics, and discourse.

Student learning goals

Implement parsing algorithms to extract syntactic structure from sentences.

Employ probabilistic models to provide robust, ranked syntactic analysis of sentences based on training corpora.

Become familiar with resources for syntactic, semantic, and discourse analysis, including the Penn Treebank, WordNet, FrameNet, and the Penn Discourse Treebank.

Assess the utility of different syntactic models, including constituent and dependency analyses

Understand a range of techniques for performing word sense disambiguation

Gain insight into the structure of extended spans of text or speech (discourse)

General method of instruction

Recommended preparation

Class assignments and grading


The information above is intended to be helpful in choosing courses. Because the instructor may further develop his/her plans for this course, its characteristics are subject to change without notice. In most cases, the official course syllabus will be distributed on the first day of class.
Last Update by Gina-Anne Levow
Date: 12/12/2013