Learning Dynamic Context Augmentation for Global Entity Linking

Despite of the recent success of collective entity linking (EL) methods, these "global" inference methods may yield sub-optimal results when the "all-mention coherence" assumption breaks, and often suffer from high computational cost at the inference stage, due to the complex search space. In this paper, we propose a simple yet effective solution, called Dynamic Context Augmentation (DCA), for collective EL, which requires only one pass through the mentions in a document. DCA sequentially accumulates context information to make efficient, collective inference, and can cope with different local EL models as a plug-and-enhance module. We explore both supervised and reinforcement learning strategies for learning the DCA model. Extensive experiments show the effectiveness of our model with different learning settings, base models, decision orders and attention mechanisms.

PDF Abstract IJCNLP 2019 PDF IJCNLP 2019 Abstract

Results from Other Papers


Task Dataset Model Metric Name Metric Value Rank Source Paper Compare
Entity Disambiguation AIDA-CoNLL DCA-SL (2019)(et al., [2019c]) In-KB Accuracy 94.64 # 5

Methods


No methods listed for this paper. Add relevant methods here