Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy

The Auto Annotation Latent Dirichlet Allocation

Authors
Yingzhuo Xiang, Dongmei Yang, Jikun Yan
Corresponding Author
Yingzhuo Xiang
Available Online July 2015.
DOI
10.2991/icismme-15.2015.387How to use a DOI?
Keywords
LDA; auto annotation; NLP; text modeling.
Abstract

In this paper, we introduce the Auto-Annotation LDA models (aaLDA), a statistical model of non-labeled documents. This model generates the annotation of LDA automatically. We derive the annotation of LDA using a k-means methods combined with a pre-processing of the corpus. In this paper, we use aaLDA models to categorize “zhongwenshilei” corpus, which is a famous Chinese corpus. Then we make a compare with the traditional LDA methods.

Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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Volume Title
Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
Series
Advances in Intelligent Systems Research
Publication Date
July 2015
ISBN
978-94-62520-67-7
ISSN
1951-6851
DOI
10.2991/icismme-15.2015.387How to use a DOI?
Copyright
© 2015, the Authors. Published by Atlantis Press.
Open Access
This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

Cite this article

TY  - CONF
AU  - Yingzhuo Xiang
AU  - Dongmei Yang
AU  - Jikun Yan
PY  - 2015/07
DA  - 2015/07
TI  - The Auto Annotation Latent Dirichlet Allocation
BT  - Proceedings of the First International Conference on Information Sciences, Machinery, Materials and Energy
PB  - Atlantis Press
SP  - 1893
EP  - 1896
SN  - 1951-6851
UR  - https://doi.org/10.2991/icismme-15.2015.387
DO  - 10.2991/icismme-15.2015.387
ID  - Xiang2015/07
ER  -