Abstract

We describe a multifaceted approach to named entity recognition that can be deployed with minimal data resources and a handful of hours of non-expert annotation. We describe how this approach was applied in the 2016 LoReHLT evaluation and demonstrate that both statistical and rule-based approaches contribute to our performance. We also demonstrate across many languages the value of selecting the sentences to be annotated when training on small amounts of data.

Date

2018

Authors

Ryan Gabbard, Jay DeYoung, Constantine Lignos, Marjorie Freedman, Ralph Weischedel

Journal

Machine translation

Volume

32

Pages

31-43

Publisher

Springer Netherlands