Resources such as FrameNet, which provide sets of semantic frame definitions and annotated textual data that maps into the evoked frames, are important for several NLP tasks. However, they are expensive to build and, consequently, are unavailable for many languages and domains. Thus, approaches able to induce semantic frames in an unsupervised manner are highly valuable. In this paper we approach that task from a network perspective as a community detection problem that targets the identification of groups of verb instances that evoke the same semantic frame and verb arguments that play the same semantic role. To do so, we apply a graph-clustering algorithm to a graph with contextualized representations of verb instances or arguments as nodes connected by edges if the distance between them is below a threshold that defines the granularity of the induced frames. By applying this approach to the benchmark dataset defined in the context of SemEval 2019, we outperformed all of the previous approaches to the task, achieving the current state-of-the-art performance.

对于一些NLP任务来说，诸如FrameNet之类的资源非常重要，这些资源提供了语义框架定义和映射到诱发框架的带注释文本数据集。但是，它们的构建成本很高，因此无法用于许多语言和域。因此，能够以无监督方式导出语义框架的方法非常有价值。在本文中，我们从网络角度将这个任务作为社区检测问题来解决，该问题的目标是识别动词实例组，这些动词实例唤起相同的语义框架和动词自变量发挥相同的语义作用。为此，我们将图聚类算法应用于图，其中动词实例或自变量的上下文表示作为由边连接的节点，如果它们之间的距离低于定义诱导帧的粒度的阈值，则对该图进行聚类。通过将这种方法应用于在SemEval 2019上下文中定义的基准数据集，我们的性能优于以前的所有方法，均达到了当前的最新性能。