@InProceedings{danda-EtAl:2017:I17-4,
  author    = {Danda, Prathyusha  and  Mishra, Pruthwik  and  Kanneganti, Silpa  and  Lanka, Soujanya},
  title     = {IIIT-H at IJCNLP-2017 Task 4: Customer Feedback Analysis using Machine Learning and Neural Network Approaches},
  booktitle = {Proceedings of the IJCNLP 2017, Shared Tasks},
  month     = {December},
  year      = {2017},
  address   = {Taipei, Taiwan},
  publisher = {Asian Federation of Natural Language Processing},
  pages     = {155--160},
  abstract  = {The IJCNLP 2017 shared task on Customer Feedback Analysis focuses on
	classifying customer feedback into one of a predefined set of categories or
	classes. In this paper, we describe our approach to this problem and the
	results on four languages, i.e. English, French, Japanese and Spanish. Our
	system implemented a bidirectional LSTM\cite{graves2005framewise} using
	pre-trained glove\cite{pennington2014glove} and fastText\cite{joulin2016bag}
	embeddings, and SVM \cite{cortes1995support} with TF-IDF vectors for
	classifying the feedback data which is described in the later sections. We also
	tried different machine learning techniques and compared the results in this
	paper. Out of the 12 participating teams, our systems obtained 0.65, 0.86, 0.70
	and 0.56 exact accuracy score in English, Spanish, French and Japanese
	respectively. We observed that our systems perform better than the baseline
	systems in three languages while we match the baseline accuracy for Japanese on
	our submitted systems. We noticed significant improvements in Japanese in later
	experiments, matching the highest performing system that was submitted in the
	shared task, which we will discuss in this paper.},
  url       = {http://www.aclweb.org/anthology/I17-4026}
}