Domain-aware Neural Model for Sequence Labeling using Joint Learning

Recently, scholars have demonstrated empirical successes of deep learning in sequence labeling, and most of the prior works focused on the word representation inside the target sentence. Unfortunately, the global information, e.g., domain information of the target document, were ignored in the previous studies. In this paper, we propose an innovative joint learning neural network which can encapsulate the global domain knowledge and the local sentence/token information to enhance the sequence labeling model. Unlike existing studies, the proposed method employs domain labeling output as a latent evidence to facilitate tagging model and such joint embedding information is generated by an enhanced highway network. Meanwhile, a redesigned CRF layer is deployed to bridge the 'local output labels' and 'global domain information'. Various kinds of information can iteratively contribute to each other, and moreover, domain knowledge can be learnt in either supervised or unsupervised environment via the new model. Experiment with multiple data sets shows that the proposed algorithm outperforms classical and most recent state-of-the-art labeling methods.