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arXiv · 2005.01866

Soft Gazetteers for Low-Resource Named Entity Recognition

Shruti Rijhwani·Shuyan Zhou·Graham Neubig·Jaime Carbonell
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Abstract

Traditional named entity recognition models use gazetteers (lists of entities) as features to improve performance. Although modern neural network models do not require such hand-crafted features for strong performance, recent work has demonstrated their utility for named entity recognition on English data. However, designing such features for low-resource languages is challenging, because exhaustive entity gazetteers do not exist in these languages. To address this problem, we propose a method of "soft gazetteers" that incorporates ubiquitously available information from English knowledge bases, such as Wikipedia, into neural named entity recognition models through cross-lingual entity linking. Our experiments on four low-resource languages show an average improvement of 4 points in F1 score. Code and data are available at https://github.com/neulab/soft-gazetteers.

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Shruti Rijhwani, Shuyan Zhou, Graham Neubig, Jaime Carbonell. 2020-05-04. Soft Gazetteers for Low-Resource Named Entity Recognition. https://arxiv.org/abs/2005.01866

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