Domain Adaptation for Sequence Labeling Tasks with a Probabilistic Language Adaptation Model
Proceedings of the 30th International Conference on Machine Learning, PMLR 28(1):293-301, 2013.
In this paper, we propose to address the problem of domain adaptation for sequence labeling tasks via distributed representation learning by using a log-bilinear language adaptation model. The proposed neural probabilistic language model simultaneously models two different but related data distributions in the source and target domains based on induced distributed representations, which encode both generalizable and domain-specific latent features. We then use the learned dense real-valued representation as augmenting features for natural language processing systems. We empirically evaluate the proposed learning technique on WSJ and MEDLINE domains with POS tagging systems, and on WSJ and Brown corpora with syntactic chunking and name entity recognition systems. Our primary results show that the proposed domain adaptation method outperforms a number comparison methods for cross domain sequence labeling tasks.