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    <abstract>We 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.</abstract>
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%0 Conference Proceedings
%T Spanless Event Annotation for Corpus-Wide Complex Event Understanding
%A Bies, Ann
%A Tracey, Jennifer
%A O’Brien, Ann
%A Chen, Song
%A Strassel, Stephanie
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F bies-etal-2024-spanless-event
%X We 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.
%U https://aclanthology.org/2024.lrec-main.1313
%P 15105-15113

Markdown (Informal)

[Spanless Event Annotation for Corpus-Wide Complex Event Understanding](https://aclanthology.org/2024.lrec-main.1313) (Bies et al., LREC-COLING 2024)

• Spanless Event Annotation for Corpus-Wide Complex Event Understanding (Bies et al., LREC-COLING 2024)

ACL

• Ann Bies, Jennifer Tracey, Ann O’Brien, Song Chen, and Stephanie Strassel. 2024. Spanless Event Annotation for Corpus-Wide Complex Event Understanding. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 15105–15113, Torino, Italia. ELRA and ICCL.