We propose a novel clustering pipeline to detect and characterize influence
campaigns from documents. This approach clusters parts of document, detects
clusters that likely reflect an influence campaign, and then identifies
documents linked to an influence campaign via their association with the
high-influence clusters. Our approach outperforms both the direct
document-level classification and the direct document-level clustering approach
in predicting if a document is part of an influence campaign. We propose
various novel techniques to enhance our pipeline, including using an existing
event factuality prediction system to obtain document parts, and aggregating
multiple clustering experiments to improve the performance of both cluster and
document classification. Classifying documents on the top of clustering not
only accurately extracts the parts of the documents that are relevant to
influence campaigns, but also capture influence campaigns as a coordinated and
holistic phenomenon. Our approach makes possible more fine-grained and
interpretable characterizations of influence campaigns from documents.

我们提出了一种新颖的聚类流程来检测和表征文档中的影响力运动。该方法通过聚类文档的部分，检测反映影响力运动的聚类，并通过它们与高影响力聚类的关联识别与影响力运动相关的文档。我们的方法在预测文档是否属于影响力运动方面优于直接的基于文档级别的分类和聚类方法。我们提出了各种新技术来增强我们的流程，包括使用现有的事件真实性预测系统获取文档部分，并聚合多个聚类实验来改进聚类和文档分类的性能。在聚类的基础上对文档进行分类不仅可以精确提取与影响力运动相关的文档部分，而且将影响力运动作为一种协调和整体的现象来捕捉。我们的方法使得从文档中更细粒度、可解释性更强地表征影响力运动成为可能。

文档部分聚类：从文档中检测和描述影响运动

Clustering Document Parts: Detecting and Characterizing Influence  Campaigns From Documents

Mining the latent intentions from large volumes of natural language inputs is
a key step to help data analysts design and refine Intelligent Virtual
Assistants (IVAs) for customer service and sales support. We created a flexible
and scalable clustering pipeline within the Verint Intent Manager (VIM) that
integrates the fine-tuning of language models, a high performing k-NN library
and community detection techniques to help analysts quickly surface and
organize relevant user intentions from conversational texts. The fine-tuning
step is necessary because pre-trained language models cannot encode texts to
efficiently surface particular clustering structures when the target texts are
from an unseen domain or the clustering task is not topic detection. We
describe the pipeline and demonstrate its performance and ability to scale on
three real-world text mining tasks. As deployed in the VIM application, this
clustering pipeline produces high quality results, improving the performance of
data analysts and reducing the time it takes to surface intentions from
customer service data, thereby reducing the time it takes to build and deploy
IVAs in new domains.

使用 Verint Intent Manager 中的聚类管道，将语言模型的微调和社区检测技术集成在一起，从而提高了数据分析师设计和改进 Intelligent Virtual Assistants 的效率和能力，并在三个真实世界的文本挖掘任务中表现出良好的性能和可扩展性。