Combining semantic graph and probabilistic topic models for discovering coherent topics

2019 ◽  
Vol 17 (4) ◽  
pp. 365-379
Author(s):  
Mehdi Allahyari ◽  
Seyedamin Pouriyeh ◽  
Krys Kochut
2016 ◽  
Vol 6 (1) ◽  
Author(s):  
Mirwaes Wahabzada ◽  
Anne-Katrin Mahlein ◽  
Christian Bauckhage ◽  
Ulrike Steiner ◽  
Erich-Christian Oerke ◽  
...  

Author(s):  
Murugan Anandarajan ◽  
Chelsey Hill ◽  
Thomas Nolan

Author(s):  
Dat Quoc Nguyen ◽  
Richard Billingsley ◽  
Lan Du ◽  
Mark Johnson

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.


2012 ◽  
Vol 11 (3) ◽  
pp. 203-215 ◽  
Author(s):  
Xin Chen ◽  
TingTing He ◽  
Xiaohua Hu ◽  
Yanhong Zhou ◽  
Yuan An ◽  
...  

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