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KESIRAJU, S. BURGET, L. SZŐKE, I. ČERNOCKÝ, J.
Original Title
Learning document representations using subspace multinomial model
Type
conference paper
Language
English
Original 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.
Keywords
Document representation, subspace modelling, topic identification, latent topic discovery
Authors
KESIRAJU, S.; BURGET, L.; SZŐKE, I.; ČERNOCKÝ, J.
Released
8. 9. 2016
Publisher
International Speech Communication Association
Location
San Francisco
ISBN
978-1-5108-3313-5
Book
Proceedings of Interspeech 2016
Pages from
700
Pages to
704
Pages count
5
URL
https://www.researchgate.net/publication/307889473_Learning_Document_Representations_Using_Subspace_Multinomial_Model
BibTex
@inproceedings{BUT132598, author="Santosh {Kesiraju} and Lukáš {Burget} and Igor {Szőke} and Jan {Černocký}", title="Learning document representations using subspace multinomial model", booktitle="Proceedings of Interspeech 2016", year="2016", pages="700--704", publisher="International Speech Communication Association", address="San Francisco", doi="10.21437/Interspeech.2016-1634", isbn="978-1-5108-3313-5", url="https://www.researchgate.net/publication/307889473_Learning_Document_Representations_Using_Subspace_Multinomial_Model" }