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\begin{document}

\title{Deep Learning for text in limted data settings}



\author[1]{Pathikkumar Patel }%
\author[2]{Bhargav Lad }%
\author[2]{Jinan Fiaidhi }%
\affil[1]{Lakehead University}%
\affil[2]{Affiliation not available}%


\vspace{-1em}



  
  \date{May 28, 2024}


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\begin{abstract}
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.%
\end{abstract}%



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