Improving Neural Named Entity Recognition with Gazetteers
This work addresses the challenge of enhancing NER accuracy for both high- and low-resource languages, though it is incremental as it builds on existing neural methods with added features.
The authors tackled the problem of improving neural named entity recognition by integrating gazetteer features derived from Wikidata, achieving performance gains in English, Chinese, and Russian across different resource settings.
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).