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Evaluating syntactic topic models

Grant number: 13/15353-6
Support Opportunities:Scholarships abroad - Research Internship - Doctorate
Effective date (Start): November 11, 2013
Effective date (End): May 10, 2014
Field of knowledge:Physical Sciences and Mathematics - Computer Science
Principal Investigator:Alneu de Andrade Lopes
Grantee:Thiago de Paulo Faleiros
Supervisor: Jordan Boyd-Graber
Host Institution: Instituto de Ciências Matemáticas e de Computação (ICMC). Universidade de São Paulo (USP). São Carlos , SP, Brazil
Research place: University of Maryland, College Park, United States  
Associated to the scholarship:11/23689-9 - Propagation in bipartite graphs for Topic Extraction in Data Streams, BP.DR

Abstract

We intend to evaluate the syntactic plausibility of Syntactic Topic Model (STM). The STMis a non-parametric Bayesian model of parsed documents. The STM generates words thatare both thematically and syntactically constrained, which combines the semantic insightsof topic models with the syntactic information available from parse tress. The expertise thatwe wish to obtain from STM study include the understand the non-parametric Bayesianmodelling, techniques of approximate posterior inference of probabilistic topics models,and evaluation methodology. As all this knowledge is important for the full developmentof the student doctorate project research. (AU)

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