%0 Conference Proceedings
%T Interpretable Topic Extraction and Word Embedding Learning Using Row-Stochastic DEDICOM
%+ Fraunhofer Institute for Intelligent Analysis and Information Systems (Fraunhofer IAIS)
%+ Universität Bonn = University of Bonn
%A Hillebrand, Lars
%A Biesner, David
%A Bauckhage, Christian
%A Sifa, Rafet
%< avec comité de lecture
%( Lecture Notes in Computer Science
%B 4th International Cross-Domain Conference for Machine Learning and Knowledge Extraction (CD-MAKE)
%C Dublin, Ireland
%Y Andreas Holzinger
%Y Peter Kieseberg
%Y A Min Tjoa
%Y Edgar Weippl
%I Springer International Publishing
%3 Machine Learning and Knowledge Extraction
%V LNCS-12279
%P 401-422
%8 2020-08-25
%D 2020
%R 10.1007/978-3-030-57321-8_22
%K Word embeddings
%K Topic analysis
%K Matrix factorization
%K Natural language processing
%Z Computer Science [cs]
%Z Humanities and Social Sciences/Library and information sciencesConference papers
%X 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.
%G English
%Z TC 5
%Z TC 8
%Z TC 12
%Z WG 8.4
%Z WG 8.9
%Z WG 12.9
%2 https://inria.hal.science/hal-03414746/document
%2 https://inria.hal.science/hal-03414746/file/497121_1_En_22_Chapter.pdf
%L hal-03414746
%U https://inria.hal.science/hal-03414746
%~ SHS
%~ IFIP-LNCS
%~ IFIP
%~ IFIP-TC
%~ IFIP-TC5
%~ IFIP-WG
%~ IFIP-TC12
%~ IFIP-TC8
%~ IFIP-WG8-4
%~ IFIP-WG8-9
%~ IFIP-CD-MAKE
%~ IFIP-WG12-9
%~ IFIP-LNCS-12279