We present a new local entity disambiguation system. The key to our system is
a novel approach for learning entity representations. In our approach we learn
an entity aware extension of Embedding for Language Model (ELMo) which we call
Entity-ELMo (E-ELMo). Given a paragraph containing one or more named entity
mentions, each mention is first defined as a function of the entire paragraph
(including other mentions), then they predict the referent entities. Utilizing
E-ELMo for local entity disambiguation, we outperform all of the
state-of-the-art local and global models on the popular benchmarks by improving
about 0.5\% on micro average accuracy for AIDA test-b with Yago candidate set.
The evaluation setup of the training data and candidate set are the same as our
baselines for fair comparison.

本研究提出了一种新的本地实体消岐系统，依赖于一种名为 E-ELMo 的实体感知扩展 embedding 方法，能在指定的段落中，基于其它提及的实体，预测它们所指的具体实体，并在 AIDA test-b 评测中提高 0.5% 的微平均准确率，创造了业界先进的性能表现。