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. 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.

本文提出一种新的名为 Hierarchical Latent Semantic Mapping (HLSM) 的主题生成方法，它可以自动从语料库中生成主题，并使用单词之间的关联和层次生成主题网络。实验表明，相比于现有的一些最先进的方法，HLSM 在几个文档集合上表现良好。