English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Lexicon information and pre-trained models, such as BERT, have been combinedto explore Chinese sequence labelling tasks due to their respective strengths.However, existing methods solely fuse lexicon features via a shallow and randominitialized sequence layer and do not integrate them into the bottom layers ofBERT. In this paper, we propose Lexicon Enhanced BERT (LEBERT) for Chinesesequence labelling, which integrates external lexicon knowledge into BERTlayers directly by a Lexicon Adapter layer. Compared with the existing methods,our model facilitates deep lexicon knowledge fusion at the lower layers ofBERT. Experiments on ten Chinese datasets of three tasks including Named EntityRecognition, Word Segmentation, and Part-of-Speech tagging, show that LEBERTachieves the state-of-the-art results.

Lexicon Enhanced Chinese Sequence Labeling Using BERT Adapter