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  4. Interpretable Topic Extraction and Word Embedding Learning Using Row-Stochastic DEDICOM
 
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2020
Conference Paper
Title

Interpretable Topic Extraction and Word Embedding Learning Using Row-Stochastic DEDICOM

Abstract
The DEDICOM algorithm provides a uniquely interpretable matrix factorization method for symmetric and asymmetric square matrices. We employ a new row-stochastic variation of DEDICOM on the pointwise mutual information matrices of text corpora to identify latent topic clusters within the vocabulary and simultaneously learn interpretable word embeddings. We introduce a method to efficiently train a constrained DEDICOM algorithm and a qualitative evaluation of its topic modeling and word embedding performance.
Author(s)
Hillebrand, Lars Patrick  
Biesner, David  
Bauckhage, Christian  
Sifa, Rafet  
Mainwork
Machine Learning and Knowledge Extraction. 4th IFIP TC 5, TC 12, WG 8.4, WG 8.9, WG 12.9 International Cross-Domain Conferernce, CD-MAKE 202. Proceedings  
Conference
International Cross-Domain Conference (CD-MAKE) 2020  
International Conference on Availability, Reliability and Security (ARES) 2020  
Open Access
DOI
10.1007/978-3-030-57321-8_22
Language
English
Fraunhofer-Institut für Intelligente Analyse- und Informationssysteme IAIS  
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