@InProceedings{yang-lu-zheng:2017:EMNLP2017,
  author    = {Yang, Wei  and  Lu, Wei  and  Zheng, Vincent},
  title     = {A Simple Regularization-based Algorithm for Learning Cross-Domain Word Embeddings},
  booktitle = {Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing},
  month     = {September},
  year      = {2017},
  address   = {Copenhagen, Denmark},
  publisher = {Association for Computational Linguistics},
  pages     = {2898--2904},
  abstract  = {Learning word embeddings has received a significant amount of attention
	recently. Often, word embeddings are learned in an unsupervised manner from a
	large collection of text. The genre of the text typically plays an important
	role in the effectiveness of the resulting embeddings. How to effectively train
	word embedding models using data from different domains remains a problem that
	is less explored. In this paper, we present a simple yet effective method for
	learning word embeddings based on text from different domains. We demonstrate
	the effectiveness of our approach through extensive experiments on various
	down-stream NLP tasks.},
  url       = {https://www.aclweb.org/anthology/D17-1312}
}