Improving Neural Named Entity Recognition with Gazetteers

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2003.03072.pdf (741.7 KB)

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https://arxiv.org/abs/2003.03072

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http://hdl.handle.net/11603/17981

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UMBC Computer Science and Electrical Engineering Department
UMBC Faculty Collection

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2020-03-06

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journal articles preprints
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Song, Chan Hee; Lawrie, Dawn; Finin, Tim; Mayfield, James; Improving Neural Named Entity Recognition with Gazetteers; Computation and Language (2020); https://arxiv.org/abs/2003.03072

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Abstract

The 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).