Measuring the intensity of events is crucial for monitoring and tracking armed conflict. Advances in automated event extraction have yielded massive data sets of "who did what to whom" micro-records that enable data-driven approaches to monitoring conflict. The Goldstein scale is a widely-used expert-based measure that scores events on a conflictual-cooperative scale. It is based only on the action category ("what") and disregards the subject ("who") and object ("to whom") of an event, as well as contextual information, like associated casualty count, that should contribute to the perception of an event's "intensity". This paper takes a latent variable-based approach to measuring conflict intensity. We introduce a probabilistic generative model that assumes each observed event is associated with a latent intensity class. A novel aspect of this model is that it imposes an ordering on the classes, such that higher-valued classes denote higher levels of intensity. The ordinal nature of the latent variable is induced from naturally ordered aspects of the data (e.g., casualty counts) where higher values naturally indicate higher intensity. We evaluate the proposed model both intrinsically and extrinsically, showing that it obtains comparatively good held-out predictive performance.

翻译：测量事件强度对于监测和追踪武装冲突至关重要。自动化事件提取技术的进步产生了大量"谁对谁做了什么"的微观记录数据集，为数据驱动的冲突监测方法提供了可能。Goldstein量表是一种广泛使用的基于专家判断的度量方法，按冲突-合作维度对事件进行评分。该量表仅基于行动类别（"做了什么"），忽略了事件的主体（"谁"）和客体（"对谁"），以及相关伤亡人数等本应影响事件"强度"感知的上下文信息。本文采用基于潜变量的方法来衡量冲突强度。我们提出了一种概率生成模型，假设每个观测事件都与一个潜在的强度类别相关联。该模型的新颖之处在于对类别施加了有序约束，使得数值更高的类别代表更高的强度水平。潜变量的有序性质源自数据中自然排序的方面（如伤亡人数），其中更高的数值自然表明更高的强度。我们通过内在和外在两种方式对该模型进行了评估，结果表明其在留出数据上获得了相对较好的预测性能。