= Topic identification, topic modelling =

[[https://is.muni.cz/auth/predmet/fi/ia161|IA161]] [[en/AdvancedNlpCourse|Advanced NLP Course]], Course Guarantee: Aleš Horák

Prepared by: Jirka Materna

== State of the Art ==
Topic modeling is a statistical approach for discovering abstract topics hidden in text documents. A document usually consists of multiple topics with different weights. Each topic can be described by typical words belonging to the topic. The most frequently used methods of topic modeling are Latent Semantic Analysis and Latent Dirichlet Allocation. 
=== References ===

 1. David M. Blei, Andrew Y. Ng, and Michael I. Jordan. Latent Dirichlet Allocation. Journal of Machine Learning Research, 3:993 – 1022, 2003.
 1. Yee W. Teh, Michael I. Jordan, Matthew J. Beal, and David M. Blei. Hierarchical Dirichlet processes . Journal of the American Statistical Association, 101:1566 – 1581, 2006.
 1. S. T. Dumais, G. W. Furnas, T. K. Landauer, S. Deerwester, and R. Harshman. Using Latent Semantic Analysis to Improve Access to Textual Information. In Proceedings of the SIGCHI Conference on Human Factors in Computing Systems, CHI ’88, pages 281–285, New York, NY, USA, 1988. ACM. ISBN 0-201-14237-6.

== Practical Session ==

In this session we will use [[http://radimrehurek.com/gensim/|Gensim]] to model latent topics of Wikipedia documents. We will focus on Latent Semantic Analysis and Latent Dirichlet Allocation models.

1. Download and extract the corpus of Czech Wikipedia documents:  [[htdocs:bigdata/wiki.tar.bz2|wiki corpus]].

1. Train LSA and LDA models of the corpus for various numbers of topics using Gensim. You can use this template: 

1. For both the LSA and LDA select the best best models    

Students will also be required to generate some results of their work and hand them in to prove completing the tasks.