Event datasets in the financial domain are often constructed based on actual application scenarios, and their event types are weakly reusable due to scenario constraints; at the same time, the massive and diverse new financial big data cannot be limited to the event types defined for specific scenarios. This limitation of a small number of event types does not meet our research needs for more complex tasks such as the prediction of major financial events and the analysis of the ripple effects of financial events. In this paper, a three-stage approach is proposed to accomplish incremental discovery of event types. For an existing annotated financial event dataset, the three-stage approach consists of: for a set of financial event data with a mixture of original and unknown event types, a semi-supervised deep clustering model with anomaly detection is first applied to classify the data into normal and abnormal events, where abnormal events are events that do not belong to known types; then normal events are tagged with appropriate event types and abnormal events are reasonably clustered. Finally, a cluster keyword extraction method is used to recommend the type names of events for the new event clusters, thus incrementally discovering new event types. The proposed method is effective in the incremental discovery of new event types on real data sets.

翻译：金融领域的事件数据集通常基于实际应用场景构建，其事件类型因场景限制而弱可复用性；同时，海量多样化的新型金融大数据无法局限于特定场景下定义的事件类型。这种少量事件类型的局限性无法满足我们对重大金融事件预测、金融事件涟漪效应分析等更复杂任务的研究需求。本文提出一种三阶段方法来实现事件类型的增量发现。针对现有标注的金融事件数据集，该方法包括：对于包含原始事件类型和未知事件类型的混合金融事件数据集合，首先应用带有异常检测的半监督深度聚类模型将数据分类为正常事件和异常事件（其中异常事件指不属于已知类型的事件）；随后对正常事件标注合适的事件类型，并对异常事件进行合理聚类；最后采用聚类关键词提取方法为新事件簇推荐事件类型名称，从而增量发现新的事件类型。在真实数据集上的实验表明，所提方法能有效实现新型事件的增量发现。