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TDJEE: A Document-Level Joint Model for Financial Event Extraction
2021
Electronics
Extracting financial events from numerous financial announcements is very important for investors to make right decisions. However, it is still challenging that event arguments always scatter in multiple sentences in a financial announcement, while most existing event extraction models only work in sentence-level scenarios. To address this problem, this paper proposes a relation-aware Transformer-based Document-level Joint Event Extraction model (TDJEE), which encodes relations between words
doi:10.3390/electronics10070824
fatcat:46rm3c2d7fa5jbumkao5uu3lhi