One, no one and one hundred thousand events: Defining and processing events in an inter-disciplinary perspective

We present an overview of event definition and processing spanning 25 years of research in NLP. We first provide linguistic background to the notion of event, and then present past attempts to formalize this concept in annotation standards to foster the development of benchmarks for event extraction systems. This ranges from MUC-3 in 1991 to the Time and Space Track challenge at SemEval 2015. Besides, we shed light on other disciplines in which the notion of event plays a crucial role, with a focus on the historical domain. Our goal is to provide a comprehensive study on event definitions and investigate which potential past efforts in the NLP community may have in a different research domain. We present the results of a questionnaire, where the notion of event for historians is put in relation to the NLP perspective

Novel Event Detection and Classification for Historical Texts

Event processing is an active area of research in the Natural Language Processing community but resources and automatic systems developed so far have mainly addressed contemporary texts. However, the recognition and elaboration of events is a crucial step when dealing with historical texts particularly in the current era of massive digitization of historical sources: research in this domain can lead to the development of methodologies and tools that can assist historians in enhancing their work, while having an impact also on the field of Natural Language Processing. Our work aims at shedding light on the complex concept of events when dealing with historical texts. More specifically, we introduce new annotation guidelines for event mentions and types, categorised into 22 classes. Then, we annotate a historical corpus accordingly, and compare two approaches for automatic event detection and classification following this novel scheme. We believe that this work can foster research in a field of inquiry so far underestimated in the area of Temporal Information Processing. To this end, we release new annotation guidelines, a corpus and new models for automatic annotation

FacTA: Evaluation of Event Factuality and Temporal Anchoring

In this paper we describe FacTA, a new task connecting the evaluation of factuality profiling and temporal anchoring, two strictly related aspects in event processing. The proposed task aims at providing a complete evaluation framework for factuality profiling, at taking the first steps in the direction of narrative container evaluation for Italian, and at making available benchmark data for high-level semantic tasks

SENTiVENT Event Annotation Guidelines v1.1

Annotation Guidelines for economic Events in the SENTiVENT project for economic news text mining. The goal of this annotation scheme is to produce a gold-standard labeled dataset for enabling supervised event extraction in the company-specific news text domain. The guidelines are based on the [Rich-ERE Guidelines][1] for [Events][2] and [Argument Fillers][3] but adapted to a corpus of business and financial news articles. We exclusively annotate event structures, unlike Rich ERE which annotates Entities and Relations separately