Self-Attention Gazetteer Embeddings for Named-Entity Recognition

Stanislav Peshterliev; Christophe Dupuy; Imre Kiss

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arXiv · 2004.04060

Self-Attention Gazetteer Embeddings for Named-Entity Recognition

Stanislav Peshterliev·Christophe Dupuy·Imre Kiss

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Abstract

Recent attempts to ingest external knowledge into neural models for named-entity recognition (NER) have exhibited mixed results. In this work, we present GazSelfAttn, a novel gazetteer embedding approach that uses self-attention and match span encoding to build enhanced gazetteer embeddings. In addition, we demonstrate how to build gazetteer resources from the open source Wikidata knowledge base. Evaluations on CoNLL-03 and Ontonotes 5 datasets, show F1 improvements over baseline model from 92.34 to 92.86 and 89.11 to 89.32 respectively, achieving performance comparable to large state-of-the-art models.

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Stanislav Peshterliev, Christophe Dupuy, Imre Kiss. 2020-04-08. Self-Attention Gazetteer Embeddings for Named-Entity Recognition. https://arxiv.org/abs/2004.04060

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