@InProceedings{song-lee-xia:2017:CoNLL,
  author    = {Song, Yan  and  Lee, Chia-Jung  and  Xia, Fei},
  title     = {Learning Word Representations with Regularization from Prior Knowledge},
  booktitle = {Proceedings of the 21st Conference on Computational Natural Language Learning (CoNLL 2017)},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {143--152},
  abstract  = {Conventional word embeddings are trained with specific criteria (e.g., based on
	language modeling or co-occurrence) inside a single information source,
	disregarding the opportunity for further calibration using external knowledge.
	This paper presents a unified framework that leverages pre-learned or external
	priors, in the form of a regularizer, for enhancing conventional language
	model-based embedding learning. We consider two types of regularizers. The
	first type is derived from topic distribution by running LDA on unlabeled data.
	The second type is based on dictionaries that are created with
	human annotation efforts. To effectively learn with the regularizers, we
	propose a novel data structure, trajectory softmax, in this paper. The
	resulting embeddings are evaluated by word similarity and sentiment
	classification. Experimental results show that our learning framework with
	regularization from prior knowledge improves embedding quality across multiple
	datasets, compared to a diverse collection of baseline methods.},
  url       = {http://aclweb.org/anthology/K17-1016}
}