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往往是根据实际应用情景构建的,其事件类型由于情景制约而难以重新使用;同时,大规模和多样化的新金融大数据不能局限于为具体情景界定的事件类型。对少数事件类型的限制不符合我们对更复杂任务的研究需要,如重大财务事件预测和金融事件波纹效应分析。在本文件中,建议采用三阶段办法,实现事件类型的递增发现。对于现有的附加说明的财务事件数据集,三阶段办法包括:对于与原始和未知事件类型混合的一套财务事件数据,先采用半监督的深度集束模型,发现异常情况,先将数据划入正常和异常事件,而异常事件不属于已知类型;然后用适当的事件类型和异常事件的合理组合来标注正常事件。最后,采用集关键词提取方法,建议新事件类别事件类别事件类型的类型,从而逐步发现新事件类型。拟议方法在逐步发现新事件类型时有效。