Song, Chan HeeLawrie, DawnFinin, TimMayfield, James2020-04-102020-04-102020-03-06Song, Chan Hee; Lawrie, Dawn; Finin, Tim; Mayfield, James; Improving Neural Named Entity Recognition with Gazetteers; Computation and Language (2020); https://arxiv.org/abs/2003.03072http://hdl.handle.net/11603/17981The goal of this work is to improve the performance of a neural named entity recognition system by adding input features that indicate a word is part of a name included in a gazetteer. This article describes how to generate gazetteers from the Wikidata knowledge graph as well as how to integrate the information into a neural NER system. Experiments reveal that the approach yields performance gains in two distinct languages: a high-resource, word-based language, English and a high-resource, character-based language, Chinese. Experiments were also performed in a low-resource language, Russian on a newly annotated Russian NER corpus from Reddit tagged with four core types and twelve extended types. This article reports a baseline score. It is a longer version of a paper in the 33rd FLAIRS conference (Song et al. 2020).8 pagesen-USThis item is likely protected under Title 17 of the U.S. Copyright Law. Unless on a Creative Commons license, for uses protected by Copyright Law, contact the copyright holder or the author.Attribution 4.0 International (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/Text