Deep Joint Entity Disambiguation with Local Neural Attention

EMNLP 2017  ·  Octavian-Eugen Ganea, Thomas Hofmann ·

We propose a novel deep learning model for joint document-level entity disambiguation, which leverages learned neural representations. Key components are entity embeddings, a neural attention mechanism over local context windows, and a differentiable joint inference stage for disambiguation. Our approach thereby combines benefits of deep learning with more traditional approaches such as graphical models and probabilistic mention-entity maps. Extensive experiments show that we are able to obtain competitive or state-of-the-art accuracy at moderate computational costs.

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Results from the Paper

Task Dataset Model Metric Name Metric Value Global Rank Result Benchmark
Entity Disambiguation ACE2004 Global Micro-F1 88.5 # 6
Entity Disambiguation AIDA-CoNLL Global In-KB Accuracy 92.22 # 14
Entity Disambiguation AQUAINT Global Micro-F1 88.5 # 5
Entity Disambiguation MSNBC Global Micro-F1 93.7 # 5
Entity Disambiguation WNED-CWEB Global Micro-F1 77.9 # 4
Entity Disambiguation WNED-WIKI Glonal Micro-F1 77.5 # 6


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