Spanless Event Annotation for Corpus-Wide Complex Event Understanding

AbstractWe present a new approach to event annotation designed to promote whole-corpus understanding of complex events in multilingual, multimedia data as part of the DARPA Knowledge-directed Artificial Intelligence Reasoning Over Schemas (KAIROS) Program. KAIROS aims to build technology capable of reasoning about complex real-world events like a specific terrorist attack in order to provide actionable insights to end users. KAIROS systems extract events from a corpus, aggregate information into a coherent semantic representation, and instantiate observed events or predict unseen but expected events using a relevant event schema selected from a generalized schema library. To support development and testing for KAIROS Phase 2B we created a complex event annotation corpus that, instead of individual event mentions anchored in document spans with pre-defined event type labels, comprises a series of temporally ordered event frames populated with information aggregated from the whole corpus and labeled with an unconstrained tag set based on Wikidata Qnodes. The corpus makes a unique contribution to the resource landscape for information extraction, addressing gaps in the availability of multilingual, multimedia corpora for schema-based event representation. The corpus will be made available through publication in the Linguistic Data Consortium (LDC) catalog.