Word embedding maps words into a low-dimensional continuous embedding space
by exploiting the local word collocation patterns in a small context window. On
the other hand, topic modeling maps documents onto a low-dimensional topic
space, by utilizing the global word collocation patterns in the same document.
These two types of patterns are complementary. In this paper, we propose a
generative topic embedding model to combine the two types of patterns. In our
model, topics are represented by embedding vectors, and are shared across
documents. The probability of each word is influenced by both its local context
and its topic. A variational inference method yields the topic embeddings as
well as the topic mixing proportions for each document. Jointly they represent
the document in a low-dimensional continuous space. In two document
classification tasks, our method performs better than eight existing methods,
with fewer features. In addition, we illustrate with an example that our method
can generate coherent topics even based on only one document.

本文提出一种生成主题嵌入模型，通过结合局部和全局单词共现模式，将局部和全局信息融合到主题表示中，从而在低维空间中表示文档，并在两个文档分类任务中表现优于八种现有方法，并且即使仅基于一个文档，也能生成连贯的主题。