Improving Neural Named Entity Recognition with Gazetteers

• URL: http://arxiv.org/abs/2003.03072v1
• Date: Fri, 6 Mar 2020 08:29:37 GMT
• Title: Improving Neural Named Entity Recognition with Gazetteers
• Authors: Chan Hee Song, Dawn Lawrie, Tim Finin, James Mayfield
• Abstract summary: 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.
• Score: 6.292153194561472
• License: http://creativecommons.org/licenses/by/4.0/
• 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).

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