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Lex-BERT: Enhancing BERT based NER with lexicons

2021-01-02 07:43:21
Wei Zhu, Daniel Cheung
arXiv_CL
         
Abstract

In this work, we represent Lex-BERT, which incorporates the lexicon information into Chinese BERT for named entity recognition (NER) tasks in a natural manner. Instead of using word embeddings and a newly designed transformer layer as in FLAT, we identify the boundary of words in the sentences using special tokens, and the modified sentence will be encoded directly by BERT. Our model does not introduce any new parameters and are more efficient than FLAT. In addition, we do not require any word embeddings accompanying the lexicon collection. Experiments on Ontonotes and ZhCrossNER show that our model outperforms FLAT and other baselines.

Abstract (translated)

URL

https://arxiv.org/abs/2101.00396

PDF

https://arxiv.org/pdf/2101.00396.pdf

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