Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation
Abstract
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.
Keywords
Cite
@article{arxiv.1908.05762,
title = {Entity-aware ELMo: Learning Contextual Entity Representation for Entity Disambiguation},
author = {Hamed Shahbazi and Xiaoli Z. Fern and Reza Ghaeini and Rasha Obeidat and Prasad Tadepalli},
journal= {arXiv preprint arXiv:1908.05762},
year = {2019}
}