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Applying Semantic Analyses to Content-Based Recommendation and Document Clustering

Speaker  Eric Rozell

Affiliation  Microsoft Research Intern

Host  Evelyne Viegas

Duration  00:42:53

Date recorded  4 August 2011

Microsoft Research Connections intern, Eric Rozell, presents the results of his research on feature generation techniques for unstructured data sources. He applies Probase—a web-scale knowledge base that was developed by Microsoft Research Asia and is generated from the Bing index, search query logs, and other sources—to extract concepts from text. He compares the performance of features generated from Probase and two other forms of semantic analysis: Explicit Semantic Analysis using Wikipedia and Latent Dirichlet Allocation. He evaluates the semantic analysis techniques on two tasks: recommendation, by using Matchbox (a platform for probabilistic recommendations from Microsoft Research Cambridge) and clustering, by using K-Means.

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