In this post, we have shown the basics of textual data analysis through word embeddings and introduced a challenging toy problem on which we could evaluate our different methods and tweaks. We’ve demonstrated the value added by a language model which incorporates semantic information. In the next part in this series, we will venture into the world of neural networks for natural language processing, covering both established technologies (such as LSTMs and CNNs) and exciting new frontiers (transformer-based architectures).
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