We have seen various different techniques so far, which employ variants of neural networks for text processing. Word-based embedding is one such common application of neural networks. As seen in the previous chapter, word-based embedding techniques are feature-level, or representation learning, problems. In other words, they solve a very specific problem: Given a text block, represent it in some feature form that is used for a downstream text mining application, such as classification, machine translation, attribute labeling, and so on. A number of machine learning techniques exist today that can apply text mining at varying accuracy levels. In this chapter, we focus on an entirely different model of text processing. We look into the core deep learning models that are suited to text processing applications and can perform both:
- Representation learning or...
