Abstract
Most of the recently proposed neural models
for named entity recognition have been purely
data-driven, with a strong emphasis on getting rid of the efforts for collecting external
resources or designing hand-crafted features.
This could increase the chance of overfitting
since the models cannot access any supervision signal beyond the small amount of annotated data, limiting their power to generalize
beyond the annotated entities. In this work, we
show that properly utilizing external gazetteers
could benefit segmental neural NER models.
We add a simple module on the recently proposed hybrid semi-Markov CRF architecture
and observe some promising results.