Detail publikace

Learning document representations using subspace multinomial model

KESIRAJU, S. BURGET, L. SZŐKE, I. ČERNOCKÝ, J.

Originální název

Learning document representations using subspace multinomial model

Typ

článek ve sborníku ve WoS nebo Scopus

Jazyk

angličtina

Originální abstrakt

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.

Klíčová slova

Document representation, subspace modelling, topic identification, latent topic discovery

Autoři

KESIRAJU, S.; BURGET, L.; SZŐKE, I.; ČERNOCKÝ, J.

Vydáno

8. 9. 2016

Nakladatel

International Speech Communication Association

Místo

San Francisco

ISBN

978-1-5108-3313-5

Kniha

Proceedings of Interspeech 2016

Strany od

700

Strany do

704

Strany počet

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"
}