@InProceedings{mishra-EtAl:2017:I17-4,
  author    = {Mishra, Pruthwik  and  Danda, Prathyusha  and  Kanneganti, Silpa  and  Lanka, Soujanya},
  title     = {IIIT-H at IJCNLP-2017 Task 3: A Bidirectional-LSTM Approach for Review Opinion Diversification},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {53--58},
  abstract  = {The Review Opinion Diversification (Revopid-2017) shared task focuses on
	selecting top-k reviews from a set of reviews for a particular product based on
	a specific criteria. In this paper, we describe our approaches and results for
	modeling the ranking of reviews based on their usefulness score, this being the
	first of the three subtasks under this shared task. Instead of posing this as a
	regression problem, we modeled this as a classification task where we want to
	identify whether a review is useful or not. We employed a bi-directional LSTM
	to represent each review and is used with a softmax layer to predict the
	usefulness score. We chose the review with highest usefulness score, then find
	its cosine similarity score with rest of the reviews. This is done in order to
	ensure diversity in the selection of top-k reviews. On the top-5 list
	prediction, we finished $3\^{}{rd}$ while in top-10 list one, we are placed
	$2\^{}{nd}$ in the shared task. We have discussed the model and the results in
	detail in the paper.},
  url       = {http://www.aclweb.org/anthology/I17-4008}
}