Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
Table 2: Results on TAC 2010 and AIDA test-b
['models', 'aida (HP)', 'aida (YAGO+KB)', 'tac (KB)']
Our baselines include a large number of state-of-the-art local (seven) and global (eight) models proposed in recent years. In addition, we also consider an alternative NED system based on the original ELMo. In particular, this model, referred to as ELMOo, is identical to our local NED model except that E-ELMo is replaced with the original ELMo and each entity e is represented by averaging the sentence-level representations of all query mentions linking to e in Wikipedia using the original ELMo. As shown in the table our models see only slight reduction in performance when prior and lexical features are removed. In contrast, the local attention based model suffered a substantial performance dip. This suggests that the contextual dependencies captured by E-ELMo are significantly richer, possibly because E-ELMo not only captures syntactic and semantic dependencies but also captures lexical and prior dependencies between the mention and the entity with the help of the char CNN in the first layer.