The Auto Annotation Latent Dirichlet Allocation

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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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  -