Character-level BERT pre-trained in Chinese suffers a limitation of lacking lexicon information, which shows effectiveness for Chinese NER. To integrate the lexicon into pre-trained LMs for Chinese NER, we investigate a semisupervised entity enhanced BERT pre-training method. In particular, we first extract an entity lexicon from the relevant raw text using a newword discovery method. We then integrate the entity information into BERT using Char-Entity-Transformer, which augments the selfattention using a combination of character and entity representations. In addition, an entity classification task helps inject the entity information into model parameters in pre-training. The pre-trained models are used for NER finetuning. Experiments on a news dataset and two datasets annotated by ourselves for NER in long-text show that our method is highly effective and achieves the best results.

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Two novel carbazole disulfonamide-diamide macrocycles 1 and 2 with the semi-flexible meta-xylyl linkages were designed, synthesized, and assessed for their anion binding properties, via 1H NMR and UV-vis titration studies....

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