Hierarchical Latent Semantic Mapping for Automated Topic Generation

DOI

10.2991/ijndc.2016.4.2.6How to use a DOI?

Keywords

Topic modeling, Network, LDA, Unsupervised learning

Abstract

Much of information sits in an unprecedented amount of text data. Managing allocation of these large scale text data is an important problem for many areas. Topic modeling performs well in this problem. The traditional generative models (PLSA,LDA) are the state-of-the-art approaches in topic modeling and most recent research on topic generation has been focusing on improving or extending these models. However, results of traditional generative models are sensitive to the number of topics K, which must be specified manually and determines the rank of solution space for topic generation. The problem of generating topics from corpus resembles community detection in networks. Many effective algorithms can automatically detect communities from networks without a manually specified number of the communities. Inspired by these algorithms, in this paper, we propose a novel method named Hierarchical Latent Semantic Mapping (HLSM), which automatically generates topics from corpus. HLSM calculates the association between each pair of words in the latent topic space, then constructs a unipartite network of words with this association and hierarchically generates topics from this network. We apply HLSM to several document collections and the experimental comparisons against several state-of-the-art approaches demonstrate the promising performance.

Copyright

© 2017, 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  - JOUR
AU  - Guorui Zhou
AU  - Guang Chen
PY  - 2016
DA  - 2016/04/01
TI  - Hierarchical Latent Semantic Mapping for Automated Topic Generation
JO  - International Journal of Networked and Distributed Computing
SP  - 127
EP  - 136
VL  - 4
IS  - 2
SN  - 2211-7946
UR  - https://doi.org/10.2991/ijndc.2016.4.2.6
DO  - 10.2991/ijndc.2016.4.2.6
ID  - Zhou2016
ER  -