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Ninth International Network Conference (INC 2012)

Ninth International Network Conference (INC 2012)
Port Elizabeth, South Africa, July 11-12, 2012
ISBN: 978-1-84102-315-1

Title: Wikipedia-Based Efficient Sampling Approach for Topic Model
Author(s): Tong Zhao, Chunping Li, Mengya Li
Reference: pp217-227
Keywords: Gibbs sampling, Latent Dirichlet Allocation, Wikipedia, Topic Model
Abstract: In this paper, we propose a novel approach called Wikipedia-based Collapsed Gibbs sampling (Wikipedia-based CGS) to improve the efficiency of the collapsed Gibbs sampling(CGS), which has been widely used in latent Dirichlet Allocation (LDA) model. Conventional CGS method views each word in the documents as an equal status for the topic modeling. Moreover, sampling all the words in the documents always leads to high computational complexity. Considering this crucial drawback of LDA we propose the Wikipedia-based CGS approach that commits to extracting more meaningful topics and improving the efficiency of the sampling process in LDA by distinguishing different statuses of words in the documents for sampling topics with Wikipedia as the background knowledge. The experiments on real world datasets show that our Wikipedia-based approach for collapsed Gibbs sampling can significantly improve the efficiency and have a better perplexity compared to existing approaches.
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