Topeax改进主题聚类，自动发现最优簇数并融合词频与语义重要性。
Topeax -- An Improved Clustering Topic Model with Density Peak Detection and Lexical-Semantic Term Importance
- 通过密度峰值检测自动确定最佳聚类数，减少对超参数依赖。
- 结合词频与语义距离计算关键词重要性，提升主题一致性。
- 在不同数据规模下更稳定，适合需要可靠主题发现的场景。
文本聚类是当前学术界和工业界主流的主题建模范式。尽管聚类主题模型表现良好，但Top2Vec和BERTopic仍存在若干未解决的问题：首先，其聚类结果对样本量和超参数高度敏感，默认值常导致次优表现；其次，BERTopic忽略关键词与主题向量间的语义距离，而Top2Vec忽略语料中的词频信息，导致主题包含停用词、噪声词，缺乏多样性与可信度。本文提出新方法Topeax，通过密度估计的峰值自动识别聚类数量，并融合词汇与语义指标计算词重要性，以获取高质量主题关键词。实验表明，Topeax在聚类恢复与描述能力上优于Top2Vec和BERTopic，且对样本量与超参数变化表现出更低的波动性。
原文摘要 · Abstract (English)
Text clustering is today the most popular paradigm for topic modelling, both in academia and industry. Despite clustering topic models' apparent success, we identify a number of issues in Top2Vec and BERTopic, which remain largely unsolved. Firstly, these approaches are unreliable at discovering natural clusters in corpora, due to extreme sensitivity to sample size and hyperparameters, the default values of which result in suboptimal behaviour. Secondly, when estimating term importance, BERTopic ignores the semantic distance of keywords to topic vectors, while Top2Vec ignores word counts in the corpus. This results in, on the one hand, less coherent topics due to the presence of stop words and junk words, and lack of variety and trust on the other. In this paper, I introduce a new approach, \textbf{Topeax}, which discovers the number of clusters from peaks in density estimates, and combines lexical and semantic indices of term importance to gain high-quality topic keywords. Topeax is demonstrated to be better at both cluster recovery and cluster description than Top2Vec and BERTopic, while also exhibiting less erratic behaviour in response to changing sample size and hyperparameters.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。