Learning Dynamic Context Augmentation for Global Entity Linking
By Xiyuan Yang, Xiaotao Gu, Sheng Lin, Siliang Tang, Yueting Zhuang, Fei Wu, Zhigang Chen, Guoping Hu, Xiang Ren. 2019
71 citations recorded by the source corpus.
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.
Subfields: Artificial Intelligence, Management Science and Operations Research, Natural Language Processing, Information Extraction
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Citations in the corpus
- Evaluating the Impact of Knowledge Graph Context on Entity Disambiguation Models (2020, methodology): “Generalizing KGContext:We induced 1-hop KG context in DCASL model [12] for candidate entities.”
- Improving Entity Linking by Modeling Latent Entity Type Information (2020, methodology): “Notice that Yang et al. (2019) achieves a high performance on in-domain AIDA-CoNLL test set, but doesn’t perform well on five out-domain test sets (even inferior than Ganea and Hofmann (2017)).”