@InProceedings{niu-EtAl:2017:Long,
  author    = {Niu, Yilin  and  Xie, Ruobing  and  Liu, Zhiyuan  and  Sun, Maosong},
  title     = {Improved Word Representation Learning with Sememes},
  booktitle = {Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)},
  month     = {July},
  year      = {2017},
  address   = {Vancouver, Canada},
  publisher = {Association for Computational Linguistics},
  pages     = {2049--2058},
  abstract  = {Sememes are minimum semantic units of word meanings, and the meaning of each
	word sense is typically composed by several sememes. Since sememes are not
	explicit for each word, people manually annotate word sememes and form
	linguistic common-sense knowledge bases. In this paper, we present that, word
	sememe information can improve word representation learning (WRL), which maps
	words into a low-dimensional semantic space and serves as a fundamental step
	for many NLP tasks. The key idea is to utilize word sememes to capture exact
	meanings of a word within specific contexts accurately. More specifically, we
	follow the framework of Skip-gram and present three sememe-encoded models to
	learn representations of sememes, senses and words, where we apply the
	attention scheme to detect word senses in various contexts. We conduct
	experiments on two tasks including word similarity and word analogy, and our
	models significantly outperform baselines. The results indicate that WRL can
	benefit from sememes via the attention scheme, and also confirm our models
	being capable of correctly modeling sememe information.},
  url       = {http://aclweb.org/anthology/P17-1187}
}