On Event Individuation for Document-Level Information Extraction
As information extraction (IE) systems have grown more adept at processing whole documents, the classic task of template filling has seen renewed interest as benchmark for document-level IE. In this position paper, we call into question the suitability of template filling for this purpose. We argue that the task demands definitive answers to thorny questions of event individuation -- the problem of distinguishing distinct events -- about which even human experts disagree. Through an annotation study and error analysis, we show that this raises concerns about the usefulness of template filling metrics, the quality of datasets for the task, and the ability of models to learn it. Finally, we consider possible solutions.
Code (1)
Tasks
PositionSimilar Papers 제목 키워드 기반
Harvesting Events from Multiple Sources: Towards a Cross-Document Event Extraction Paradigm
Document-level event extraction aims to extract structured event information from unstructured text. However, a single document often contains limited event information and the roles of different event arguments may be b…
coreference-resolutionCoreference ResolutionDocument-level Event ExtractionEvent ExtractionReconstructing Event Regions for Event Extraction via Graph Attention Networks
Event information is usually scattered across multiple sentences within a document. The local sentence-level event extractors often yield many noisy event role filler extractions in the absence of a broader view of the d…
Event ExtractionGraph AttentionSentenceExploiting Data Characteristics for Document-level Event Extraction
Document-level event extraction (DEE) extracts structured information of events from a document. Previous studies focus on improving the model architecture. We propose to exploit data characteristics: 1) we utilize more …
Document-level Event ExtractionEvent ExtractionAn Effective System for Multi-format Information Extraction
The multi-format information extraction task in the 2021 Language and Intelligence Challenge is designed to comprehensively evaluate information extraction from different dimensions. It consists of an multiple slots rela…
DecoderDocument-level Event ExtractionEvent ExtractionMulti-Task Learning+4Joint Extraction of Events and Entities within a Document Context
Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. Existing …
Entity Extraction using GANEvent ExtractionSentence