Event extraction, a specialized stream of information extraction rooted back into the 1980s, has greatly gained in popularity due to the advent of big data and the developments in the related fields of text mining and natural language processing. However, up to this date, an overview of this particular field remains elusive. Therefore, we give a summarization of event extraction techniques for textual data, distinguishing between data-driven, knowledge-driven, and hybrid methods, and present a qualitative evaluation of these. Moreover, we discuss common decision support applications of event extraction from text corpora. Last, we elaborate on the evaluation of event extraction systems and identify current research issues. We identify data-driven, knowledge-driven, and hybrid event extraction approaches.A wide variety of decision support applications can benefit from event extraction.Pressing research issues to be addressed are scalability and domain dependencies.Evaluation with annotated data from standard benchmarks or crowdsourcing is advised.
['Frederik Hogenboom', 'Flavius Frasincar', 'Uzay Kaymak', 'Franciska de Jong', 'Emiel Caron']
A Survey of event extraction methods from text for decision support systems