@InProceedings{horn:2017:RepL4NLP,
  author    = {Horn, Franziska},
  title     = {Context encoders as a simple but powerful extension of word2vec},
  booktitle = {Proceedings of the 2nd Workshop on Representation Learning for NLP},
  month     = {August},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {10--14},
  abstract  = {With a strikingly simple architecture and the ability to learn meaningful word
	embeddings efficiently from texts containing billions of words, word2vec
	remains one of the most popular neural language models used today. However, as
	only a single embedding is learned for every word in the vocabulary, the model
	fails to optimally represent words with multiple meanings and, additionally, it
	is not possible to create embeddings for new (out-of-vocabulary) words on the
	spot. Based on an intuitive interpretation of the continuous bag-of-words
	(CBOW) word2vec model's negative sampling training objective in terms of
	predicting context based similarities, we motivate an extension of the model we
	call context encoders (ConEc). By multiplying the matrix of trained word2vec
	embeddings with a word's average context vector, out-of-vocabulary (OOV)
	embeddings and representations for words with multiple meanings can be created
	based on the words' local contexts. The benefits of this approach are
	illustrated by using these word embeddings as features in the CoNLL 2003 named
	entity recognition (NER) task.},
  url       = {http://www.aclweb.org/anthology/W17-2602}
}