amttl

ds = tfds.load('huggingface:amttl/amttl')

Chinese word segmentation (CWS) trained from open source corpus faces dramatic performance drop
when dealing with domain text, especially for a domain with lots of special terms and diverse
writing styles, such as the biomedical domain. However, building domain-specific CWS requires
extremely high annotation cost. In this paper, we propose an approach by exploiting domain-invariant
knowledge from high resource to low resource domains. Extensive experiments show that our mode
achieves consistently higher accuracy than the single-task CWS and other transfer learning
baselines, especially when there is a large disparity between source and target domains.

This dataset is the accompanied medical Chinese word segmentation (CWS) dataset.
The tags are in BIES scheme.

For more details see https://www.aclweb.org/anthology/C18-1307/

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