Machine learning for texts
Machine learning plays an important role in text processing. It allows to better understand the information hidden in the text, and extract the useful knowledge hidden there. We are already familiar with machine learning models from the previous chapters, and, in fact, we have even used some of them for texts already, for example, POS tagger and NER from Stanford CoreNLP are all machine learning based models.
In Chapters 4, Supervised Learning - Clasfication and Regression and Chapter 5, Unsupervised Learning - Clustering and Dimensionality Reduction we covered supervised and unsupervised machine learning problems. When it comes to text, both play an important role in helping to organize the texts or extract useful pieces of information. In this section, we will see how to apply them to text data.
Unsupervised learning for texts
As we know, unsupervised machine learning deals with cases when no information about labels is provided. For texts, it means just letting...
