@InProceedings{dey-mondal-das:2017:I17-4,
  author    = {Dey, Monalisa  and  Mondal, Anupam  and  Das, Dipankar},
  title     = {JUNLP at IJCNLP-2017 Task 3: A Rank Prediction Model 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     = {138--142},
  abstract  = {IJCNLP-17 Review Opinion Diversification (RevOpiD-2017) task has been designed
	for ranking the top-k reviews of a product from a set of reviews, which assists
	in identifying a summarized output to express the opinion of the entire review
	set. The task is divided into three independent subtasks as
	subtask-A,subtask-B, and subtask-C. Each of these three subtasks selects the
	top-k reviews based on helpfulness, representativeness, and exhaustiveness of
	the opinions expressed in the review set individually. In order to develop the
	modules and predict the rank of reviews for all three subtasks, we have
	employed two well-known supervised classifiers namely, Na¨ıve Bayes and
	Logistic Regression on the top of several extracted features such as the number
	of nouns, number of verbs, and number of sentiment words etc from the provided
	datasets. Finally, the organizers have helped to validate the predicted outputs
	for all three subtasks by using their evaluation metrics. The metrics provide
	the scores of list size 5 as (0.80 (mth)) for subtask-A, (0.86 (cos), 0.87 (cos
	d), 0.71 (cpr), 4.98 (a-dcg), and 556.94 (wt)) for subtask B, and (10.94 (unwt)
	and 0.67 (recall)) for subtask C individually.},
  url       = {http://www.aclweb.org/anthology/I17-4023}
}