@InProceedings{tang-EtAl:2016:COLING3,
  author    = {Tang, Duyu  and  Qin, Bing  and  Feng, Xiaocheng  and  Liu, Ting},
  title     = {Effective LSTMs for Target-Dependent Sentiment Classification},
  booktitle = {Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers},
  month     = {December},
  year      = {2016},
  address   = {Osaka, Japan},
  publisher = {The COLING 2016 Organizing Committee},
  pages     = {3298--3307},
  abstract  = {Target-dependent sentiment classification remains a challenge: modeling the
	semantic relatedness of a target with its context words in a sentence.
	Different context words have different influences on determining the sentiment
	polarity of a sentence towards the target. Therefore, it is desirable to
	integrate the connections between target word and context words when building a
	learning system. In this paper, we develop two target dependent long short-term
	memory (LSTM) models, where target information is automatically taken into
	account. We evaluate our methods on a benchmark dataset from Twitter. Empirical
	results show that modeling sentence representation with standard LSTM does not
	perform well. Incorporating target information into LSTM can significantly
	boost the classification accuracy. The target-dependent LSTM models achieve
	state-of-the-art performances without using syntactic parser or external
	sentiment lexicons.},
  url       = {http://aclweb.org/anthology/C16-1311}
}