**Department of Mathematics**

**"****Probabilistic latent semantic analysis****"**

**SAVAŞ DAYANIK**

**(BİLKENT UNIVERSITY)**

**Abstract:** This
is a sequel to my talk on factor models for text

collections. In this talk, I will go over a generative probabilistic

model that handles polysemy (namely, words with multiple meanings)

better than its deterministic counterpart. Each document is thought to

be generated by a mixture of topic-word distributions. Both topic

distributions and document-topic weights are found by the EM algorithm.

Since the number of topics is smaller than the number of terms, the

documents can be represented by their topic weights in a much smaller

subspace of term space. Each topic gives different weights to the same

terms, which turns out to be an effective way to address the polysemy

problem.

**Date: ****Thursday, November 10,
2016**

**Time: ****13:40**

**Place: ****Mathematics Seminar,
SA-141**

Tea and cookies will be served before the
seminar.