Topic modeling is pivotal in discerning hidden semantic structures within
texts, thereby generating meaningful descriptive keywords. While innovative
techniques like BERTopic and Top2Vec have recently emerged in the forefront,
they manifest certain limitations. Our analysis indicates that these methods
might not prioritize the refinement of their clustering mechanism, potentially
compromising the quality of derived topic clusters. To illustrate, Top2Vec
designates the centroids of clustering results to represent topics, whereas
BERTopic harnesses C-TF-IDF for its topic extraction.In response to these
challenges, we introduce "TF-RDF" (Term Frequency - Relative Document
Frequency), a distinctive approach to assess the relevance of terms within a
document. Building on the strengths of TF-RDF, we present MPTopic, a clustering
algorithm intrinsically driven by the insights of TF-RDF. Through comprehensive
evaluation, it is evident that the topic keywords identified with the synergy
of MPTopic and TF-RDF outperform those extracted by both BERTopic and Top2Vec.

通过引入 TF-RDF 方法和 MPTopic 聚类算法，本研究展示了通过 MPTopic 和 TF-RDF 的协同作用识别出的主题关键词优于 BERTopic 和 Top2Vec 提取的关键词。

MPTopic: 通过遮蔽置换预训练优化主题建模

MPTopic: Improving topic modeling via Masked Permuted pre-training

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。