  Named entity recognition (NER) for identifying proper nouns in unstructured
text is one of the most important and fundamental tasks in natural language
processing. However, despite the widespread use of NER models, they still
require a large-scale labeled data set, which incurs a heavy burden due to
manual annotation. Domain adaptation is one of the most promising solutions to
this problem, where rich labeled data from the relevant source domain are
utilized to strengthen the generalizability of a model based on the target
domain. However, the mainstream cross-domain NER models are still affected by
the following two challenges (1) Extracting domain-invariant information such
as syntactic information for cross-domain transfer. (2) Integrating
domain-specific information such as semantic information into the model to
improve the performance of NER. In this study, we present a semi-supervised
framework for transferable NER, which disentangles the domain-invariant latent
variables and domain-specific latent variables. In the proposed framework, the
domain-specific information is integrated with the domain-specific latent
variables by using a domain predictor. The domain-specific and domain-invariant
latent variables are disentangled using three mutual information regularization
terms, i.e., maximizing the mutual information between the domain-specific
latent variables and the original embedding, maximizing the mutual information
between the domain-invariant latent variables and the original embedding, and
minimizing the mutual information between the domain-specific and
domain-invariant latent variables. Extensive experiments demonstrated that our
model can obtain state-of-the-art performance with cross-domain and
cross-lingual NER benchmark data sets.
