Topic modeling of textual corpora is an important and challenging problem. In
most previous work, the "bag-of-words" assumption is usually made which ignores
the ordering of words. This assumption simplifies the computation, but it
unrealistically loses the ordering information and the semantic of words in the
context. In this paper, we present a Gaussian Mixture Neural Topic Model
(GMNTM) which incorporates both the ordering of words and the semantic meaning
of sentences into topic modeling. Specifically, we represent each topic as a
cluster of multi-dimensional vectors and embed the corpus into a collection of
vectors generated by the Gaussian mixture model. Each word is affected not only
by its topic, but also by the embedding vector of its surrounding words and the
context. The Gaussian mixture components and the topic of documents, sentences
and words can be learnt jointly. Extensive experiments show that our model can
learn better topics and more accurate word distributions for each topic.
Quantitatively, comparing to state-of-the-art topic modeling approaches, GMNTM
obtains significantly better performance in terms of perplexity, retrieval
accuracy and classification accuracy.

本文提出了一种高斯混合神经主题模型（GMNTM），该模型将单词的顺序和句子的语义意义同时纳入了主题建模，实验结果表明，相比现有的主题建模方法，GMNTM 在困惑度、检索准确性和分类准确性方面获得了显著的改进。