Faculty Candidate Seminar
Binary Search Trees for Sentiment Analysis
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Searching through data efficiently is an important problem in computer science. In this lecture, we introduce binary search trees, why they are important, and how to implement basic operations on them. Our discussion is motivated by an example from natural language processing, sentiment analysis (determining whether a given piece of text is positive or negative). This teaching demonstration is similar to the type of material taught in EECS 281: Data Structures and Algorithms.
Bio: Laura Burdick is a Ph.D. candidate at UM, where she researches natural language processing. She has previously been the primary instructor for a freshmen-level exploratory CS class, leading to the Rackham Outstanding GSI Award. She has also co-taught an advanced natural language processing course and led discussion sections for a data structures and algorithms course. As co-director of Girls Encoded, she is active in outreach to recruit and retain women in technology.