Project details for Latent Topic Models for Hypertext

Logo Latent Topic Models for Hypertext 1.0

by amitg - September 2, 2009, 15:40:42 CET [ Project Homepage BibTeX BibTeX for corresponding Paper Download ]

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Description:

A probabilistic generative model for hypertext document collections that explicitly models the generation of links. Specifically, links from a word w to a document d depend directly on how frequent the topic of w is in d, in addition to the in-degree of d. We show how to perform

EM learning on this model efficiently. By not modeling links as analogous to words, we end up using far less free parameters, and obtain better link prediction results.

Changes to previous version:

Initial Announcement on mloss.org.

BibTeX Entry: Download
Corresponding Paper BibTeX Entry: Download
URL: Project Homepage
Supported Operating Systems: Linux
Data Formats: None
Tags: Lda, Latent Semantic Analysis, Topic Modeling, Graphical Models, Em
Archive: download here

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