An entity-aware extension of ELMo, E-ELMo, predicts gold entities at mention positions and powers a local entity disambiguation model that achieves state-of-the-art results on AIDA and TAC 2010.
Joint Neural Entity Disambiguation with Output Space Search
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abstract
In this paper, we present a novel model for entity disambiguation that combines both local contextual information and global evidences through Limited Discrepancy Search (LDS). Given an input document, we start from a complete solution constructed by a local model and conduct a search in the space of possible corrections to improve the local solution from a global view point. Our search utilizes a heuristic function to focus more on the least confident local decisions and a pruning function to score the global solutions based on their local fitness and the global coherences among the predicted entities. Experimental results on CoNLL 2003 and TAC 2010 benchmarks verify the effectiveness of our model.
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cs.CL 1years
2019 1verdicts
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Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
An entity-aware extension of ELMo, E-ELMo, predicts gold entities at mention positions and powers a local entity disambiguation model that achieves state-of-the-art results on AIDA and TAC 2010.