Topic modeling is used for discovering latent semantic structure, usually
referred to as topics, in a large collection of documents. The most widely used
methods are Latent Dirichlet Allocation and Probabilistic Latent Semantic
Analysis. Despite their popularity they have several weaknesses. In order to
achieve optimal results they often require the number of topics to be known,
custom stop-word lists, stemming, and lemmatization. Additionally these methods
rely on bag-of-words representation of documents which ignore the ordering and
semantics of words. Distributed representations of documents and words have
gained popularity due to their ability to capture semantics of words and
documents. We present $\texttt{top2vec}$, which leverages joint document and
word semantic embedding to find $\textit{topic vectors}$. This model does not
require stop-word lists, stemming or lemmatization, and it automatically finds
the number of topics. The resulting topic vectors are jointly embedded with the
document and word vectors with distance between them representing semantic
similarity. Our experiments demonstrate that $\texttt{top2vec}$ finds topics
which are significantly more informative and representative of the corpus
trained on than probabilistic generative models.

提出了一种基于分布式语义嵌入的主题建模方法 top2vec，不需要预定义的停用词表、词干提取或词形还原等预处理，能够自动确定主题数目，有效地提取语义信息并以主题向量的形式呈现。实验结果表明，top2vec 比传统的生成模型更加优秀。

Top2Vec：主题的分布式表示

Top2Vec: Distributed Representations of Topics

Probabilistic Latent Semantic Analysis is a novel statistical technique for
the analysis of two-mode and co-occurrence data, which has applications in
information retrieval and filtering, natural language processing, machine
learning from text, and in related areas. Compared to standard Latent Semantic
Analysis which stems from linear algebra and performs a Singular Value
Decomposition of co-occurrence tables, the proposed method is based on a
mixture decomposition derived from a latent class model. This results in a more
principled approach which has a solid foundation in statistics. In order to
avoid overfitting, we propose a widely applicable generalization of maximum
likelihood model fitting by tempered EM. Our approach yields substantial and
consistent improvements over Latent Semantic Analysis in a number of
experiments.

该文章介绍了概率隐含语义分析技术，其应用于信息检索和过滤、自然语言处理和文本机器学习等领域，该方法是一种基于潜在类模型的混合分解，避免了过度拟合，相比于标准的潜在语义分析方法，在多个实验中得到了显著且一致的改进。