  Named entity recognition (NER) is a foundational technology for information
extraction. This paper presents a flexible NER framework compatible with
different languages and domains. Inspired by the idea of distant supervision
(DS), this paper enhances the representation by increasing the entity-context
diversity without relying on external resources. We choose different layer
stacks and sub-network combinations to construct the bilateral networks. This
strategy can generally improve model performance on different datasets. We
conduct experiments on five languages, such as English, German, Spanish, Dutch
and Chinese, and biomedical fields, such as identifying the chemicals and
gene/protein terms from scientific works. Experimental results demonstrate the
good performance of this framework.
