The explosion in the amount of news and journalistic content being generated
across the globe, coupled with extended and instantaneous access to information
through online media, makes it difficult and time-consuming to monitor news
developments and opinion formation in real time. There is an increasing need
for tools that can pre-process, analyse and classify raw text to extract
interpretable content; specifically, identifying topics and content-driven
groupings of articles. We present here such a methodology that brings together
powerful vector embeddings from Natural Language Processing with tools from
Graph Theory that exploit diffusive dynamics on graphs to reveal natural
partitions across scales. Our framework uses a recent deep neural network text
analysis methodology (Doc2vec) to represent text in vector form and then
applies a multi-scale community detection method (Markov Stability) to
partition a similarity graph of document vectors. The method allows us to
obtain clusters of documents with similar content, at different levels of
resolution, in an unsupervised manner. We showcase our approach with the
analysis of a corpus of 9,000 news articles published by Vox Media over one
year. Our results show consistent groupings of documents according to content
without a priori assumptions about the number or type of clusters to be found.
The multilevel clustering reveals a quasi-hierarchy of topics and subtopics
with increased intelligibility and improved topic coherence as compared to
external taxonomy services and standard topic detection methods.

介绍了一种利用自然语言处理和图论工具结合的方法，通过向量表示和多尺度社团检测技术对文本进行分析和分类，得出了一组组具有相似内容的文档，揭示了主题和子主题的类层次结构。

基于多尺度图划分的内容驱动非监督式新闻文章聚类

Content-driven, unsupervised clustering of news articles through  multiscale graph partitioning

We propose a novel approach to the problem of multilevel clustering, which
aims to simultaneously partition data in each group and discover grouping
patterns among groups in a potentially large hierarchically structured corpus
of data. Our method involves a joint optimization formulation over several
spaces of discrete probability measures, which are endowed with Wasserstein
distance metrics. We propose a number of variants of this problem, which admit
fast optimization algorithms, by exploiting the connection to the problem of
finding Wasserstein barycenters. Consistency properties are established for the
estimates of both local and global clusters. Finally, experiment results with
both synthetic and real data are presented to demonstrate the flexibility and
scalability of the proposed approach.

本文提出一种新的方法来解决多层次聚类问题，该方法旨在同时将数据在每个组中分区，并在潜在的大型分层结构数据集中发现组间的分组模式。我们的方法涉及到多个离散概率测度空间上的联合优化方案，这些测度空间具有 Wasserstein 距离度量。通过利用与 Wasserstein barycenter 问题的联系，我们提出了该问题的许多变体，这些变体可以采用快速的优化算法。本文还建立了局部和全局聚类估计的一致性性质。最后，使用合成和真实数据展示了所提出方法的灵活性和可扩展性。

Wasserstein 均值下的多层聚类

Multilevel Clustering via Wasserstein Means

We present a Bayesian nonparametric framework for multilevel clustering which
utilizes group-level context information to simultaneously discover
low-dimensional structures of the group contents and partitions groups into
clusters. Using the Dirichlet process as the building block, our model
constructs a product base-measure with a nested structure to accommodate
content and context observations at multiple levels. The proposed model
possesses properties that link the nested Dirichlet processes (nDP) and the
Dirichlet process mixture models (DPM) in an interesting way: integrating out
all contents results in the DPM over contexts, whereas integrating out
group-specific contexts results in the nDP mixture over content variables. We
provide a Polya-urn view of the model and an efficient collapsed Gibbs
inference procedure. Extensive experiments on real-world datasets demonstrate
the advantage of utilizing context information via our model in both text and
image domains.

本文提出了一种贝叶斯非参数多层聚类框架，利用群组水平上下文信息同时发现群组内容的低维结构和将群组分成簇，实验结果表明，该模型在文本和图像领域中利用上下文信息具有优势。