EventFull: Complete and Consistent Event Relation Annotation

• URL: http://arxiv.org/abs/2412.12733v1
• Date: Tue, 17 Dec 2024 09:55:41 GMT
• Title: EventFull: Complete and Consistent Event Relation Annotation
• Authors: Alon Eirew, Eviatar Nachshoni, Aviv Slobodkin, Ido Dagan,
• Abstract summary: textitEventFull is a tool that supports consistent, complete and efficient annotation of temporal, causal and coreference relations. A pilot study demonstrates that EventFull accelerates and simplifies the annotation process while yielding high inter-annotator agreement.
• Score: 16.089136332919487
• License:
• Abstract: Event relation detection is a fundamental NLP task, leveraged in many downstream applications, whose modeling requires datasets annotated with event relations of various types. However, systematic and complete annotation of these relations is costly and challenging, due to the quadratic number of event pairs that need to be considered. Consequently, many current event relation datasets lack systematicity and completeness. In response, we introduce \textit{EventFull}, the first tool that supports consistent, complete and efficient annotation of temporal, causal and coreference relations via a unified and synergetic process. A pilot study demonstrates that EventFull accelerates and simplifies the annotation process while yielding high inter-annotator agreement.

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