Faculty of Information Technology, BUT

Publication Details

Learning document representations using subspace multinomial model

KESIRAJU Santosh, BURGET Lukáš, SZŐKE Igor and ČERNOCKÝ Jan. Learning document representations using subspace multinomial model. In: Proceedings of Interspeech 2016. San Francisco: International Speech Communication Association, 2016, pp. 700-704. ISBN 978-1-5108-3313-5. Available from: https://www.researchgate.net/publication/307889473_Learning_Document_Representations_Using_Subspace_Multinomial_Model
Czech title
Učení reprezentací dokumentů pomocí podprostorového multinomiálního modelu
Type
conference paper
Language
english
Authors
URL
Keywords
Document representation, subspace modelling, topic identification, latent topic discovery
Abstract
Subspace multinomial model (SMM) is a log-linear model and can be used for learning low dimensional continuous representation for discrete data. SMMand its variants have been used for speaker verification based on prosodic features and phonotactic language recognition. In this paper, we propose a new variant of SMM that introduces sparsity and call the resulting model as `1 SMM. We show that `1 SMM can be used for learning document representations that are helpful in topic identification or classification and clustering tasks. Our experiments in document classification show that SMM achieves comparable results to models such as latent Dirichlet allocation and sparse topical coding, while having a useful property that the resulting document vectors are Gaussian distributed.
Annotation
Subspace multinomial model (SMM) is a log-linear model and can be used for learning low dimensional continuous representation for discrete data. SMMand its variants have been used for speaker verification based on prosodic features and phonotactic language recognition. In this paper, we propose a new variant of SMM that introduces sparsity and call the resulting model as `1 SMM. We show that `1 SMM can be used for learning document representations that are helpful in topic identification or classification and clustering tasks. Our experiments in document classification show that SMM achieves comparable results to models such as latent Dirichlet allocation and sparse topical coding, while having a useful property that the resulting document vectors are Gaussian distributed.
Published
2016
Pages
700-704
Proceedings
Proceedings of Interspeech 2016
Conference
Interspeech 2016, San Francisco, US
ISBN
978-1-5108-3313-5
Publisher
International Speech Communication Association
Place
San Francisco, US
DOI
BibTeX
@INPROCEEDINGS{FITPUB11269,
   author = "Santosh Kesiraju and Luk\'{a}\v{s} Burget and Igor Sz\H{o}ke and Jan \v{C}ernock\'{y}",
   title = "Learning document representations using subspace multinomial model",
   pages = "700--704",
   booktitle = "Proceedings of Interspeech 2016",
   year = 2016,
   location = "San Francisco, US",
   publisher = "International Speech Communication Association",
   ISBN = "978-1-5108-3313-5",
   doi = "10.21437/Interspeech.2016-1634",
   language = "english",
   url = "https://www.fit.vut.cz/research/publication/11269"
}
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