Inferring Functional Groups from Microbial Gene Catalogue with Probabilistic Topic Models

2011 ◽  
Author(s):  
Xin Chen ◽  
Ting Ting He ◽  
Xiaohua Hu ◽  
Yuan An ◽  
Xindong Wu
Keyword(s):  
Functional Groups ◽  
Topic Models ◽  
Probabilistic Topic Models ◽  
Gene Catalogue

Estimating Functional Groups in Human Gut Microbiome With Probabilistic Topic Models

2012 ◽  
Vol 11 (3) ◽  
pp. 203-215 ◽  
Author(s):  
Xin Chen ◽  
TingTing He ◽  
Xiaohua Hu ◽  
Yanhong Zhou ◽  
Yuan An ◽  
...  
Keyword(s):  
Functional Groups ◽  
Gut Microbiome ◽  
Topic Models ◽  
Human Gut ◽  
Human Gut Microbiome ◽  
Probabilistic Topic Models

scholarly journals Plant Phenotyping using Probabilistic Topic Models: Uncovering the Hyperspectral Language of Plants

2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Mirwaes Wahabzada ◽  
Anne-Katrin Mahlein ◽  
Christian Bauckhage ◽  
Ulrike Steiner ◽  
Erich-Christian Oerke ◽  
...  
Keyword(s):  
Topic Models ◽  
Plant Phenotyping ◽  
Probabilistic Topic Models

Probabilistic Topic Models

2018 ◽  
pp. 117-130 ◽  
Author(s):  
Murugan Anandarajan ◽  
Chelsey Hill ◽  
Thomas Nolan
Keyword(s):  
Topic Models ◽  
Probabilistic Topic Models

scholarly journals Improving Topic Models with Latent Feature Word Representations

2015 ◽  
Vol 3 ◽  
pp. 299-313 ◽  
Author(s):  
Dat Quoc Nguyen ◽  
Richard Billingsley ◽  
Lan Du ◽  
Mark Johnson
Keyword(s):  
High Performance ◽  
Feature Vector ◽  
Topic Models ◽  
Document Collections ◽  
Probabilistic Topic Models ◽  
New Models ◽  
Classification Tasks ◽  
Latent Topics ◽  
Vector Representations ◽  
Feature Word

Probabilistic topic models are widely used to discover latent topics in document collections, while latent feature vector representations of words have been used to obtain high performance in many NLP tasks. In this paper, we extend two different Dirichlet multinomial topic models by incorporating latent feature vector representations of words trained on very large corpora to improve the word-topic mapping learnt on a smaller corpus. Experimental results show that by using information from the external corpora, our new models produce significant improvements on topic coherence, document clustering and document classification tasks, especially on datasets with few or short documents.

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