HighIE: High-Order Inference for Entity Recognition, Relation Extraction, and Event Extraction

Abstract

Most prior work on Information Extraction typically predicts labels of individual instances (e.g., event triggers, relations, entities) independently regardless of their interactions. We propose a novel framework, HighIE, that aims to integrate high-order cross-subtask and cross-instance dependencies in both learning and inference. High-order inference on label variables is an NP-hard problem. To address it, we propose a high-order decoder that is unfolded from an approximate inference algorithm. The experimental results show that our approach achieves consistent improvement compared with prior work.

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