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Deep Learning for text in limted data settings
  • Pathikkumar Patel ,
  • Bhargav Lad ,
  • Jinan Fiaidhi
Pathikkumar Patel
Lakehead University

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Bhargav Lad
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Jinan Fiaidhi
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During the last few years, RNN models have been extensively used and they have proven to be better for sequence and text data. RNNs have achieved state-of-the-art performance levels in several applications such as text classification, sequence to sequence modelling and time series forecasting. In this article we will review different Machine Learning and Deep Learning based approaches for text data and look at the results obtained from these methods. This work also explores the use of transfer learning in NLP and how it affects the performance of models on a specific application of sentiment analysis.