Towards Improving Neural Named Entity Recognition with Gazetteers - Liu, Tianyu; Yao, Jin-Ge; Lin, Chin-Yew (Meeting of the Association for Computational Linguistics, 2019)
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.