Summary

Natural language processing enables a machine to understand the language of humans, and just as we learned how to comprehend and process language, machines are taught as well. Two ways of better understanding language that allow machines to contribute to the real world are POS tagging and named entity recognition.

The former is the process of assigning POS tags to individual words so that the machine can learn context, and the latter is recognizing and categorizing named entities to extract valuable information from corpora.

There are distinctions in the way these processes are performed: the algorithms can be supervised or unsupervised, and the approach can be rule-based or stochastic. Either way, the goal is the same, that is, to comprehend and communicate with humans in their natural language.

In the next chapter, we will be discussing neural networks, how they work, and how they can be used for natural language processing.