Evaluating the word-expert approach for Named-Entity Disambiguation
Computation and Language
2016-03-16 v1
Abstract
Named Entity Disambiguation (NED) is the task of linking a named-entity mention to an instance in a knowledge-base, typically Wikipedia. This task is closely related to word-sense disambiguation (WSD), where the supervised word-expert approach has prevailed. In this work we present the results of the word-expert approach to NED, where one classifier is built for each target entity mention string. The resources necessary to build the system, a dictionary and a set of training instances, have been automatically derived from Wikipedia. We provide empirical evidence of the value of this approach, as well as a study of the differences between WSD and NED, including ambiguity and synonymy statistics.
Cite
@article{arxiv.1603.04767,
title = {Evaluating the word-expert approach for Named-Entity Disambiguation},
author = {Angel X. Chang and Valentin I. Spitkovsky and Christopher D. Manning and Eneko Agirre},
journal= {arXiv preprint arXiv:1603.04767},
year = {2016}
}