ASV-Label
Login

16px-feed-icon Veröffentlichungen View this page in English

NieklerJaehnichen2012

Abstract:

Many approaches have been introduced to enable Latent Dirichlet Allocation (LDA) models to be updated in an online manner. This includes inferring new documents into the model, passing parameter priors to the inference algorithm or a mixture of both, leading to more complicated and computationally expensive models. We present a method to match and compare the resulting LDA topics of different models with light weight easy to use similarity measures. We address the on-line problem by keeping the model inference simple and matching topics solely by their high probability word lists.

Link to proceedings

Type: Inproceedings

Author: Andreas Niekler and Patrick Jähnichen
Title: Matching Results of Latent Dirichlet Allocation for Text
Booktitle: Proceedings of ICCM 2012, 11th International Conference on Cognitive Modeling
Year: 2012
Pages:317-322
Publisher:Universitätsverlag der TU Berlin
Address:
@INPROCEEDINGS{NieklerJaehnichen2012,
AUTHOR = {Andreas Niekler and Patrick Jähnichen},
TITLE = {Matching Results of Latent Dirichlet Allocation for Text},
BOOKTITLE = {Proceedings of ICCM 2012, 11th International Conference on Cognitive Modeling},
YEAR = {2012},
PAGES = {317-322},
PUBLISHER = {Universitätsverlag der TU Berlin}
}