Natural language processing is a rich field of study with many advanced techniques and wide applications in ML, computational linguistics, and artificial intelligence. In this chapter, however, we focused on the specific tools and tactics that are most prevalent in everyday ML tasks.
The techniques presented in this chapter are building blocks that can be mixed and matched in order to achieve many different outcomes. Using the information in this chapter alone, you can build a simple full-text search engine, an intent extractor for spoken or written commands, an article summarizer, and many other impressive tools. However, the most impressive applications of NLP arise when these techniques are combined with advanced learning models, such as ANNs and RNNs.
In particular, you learned about word metrics, such as string distance and TF-IDF relevance scoring; preprocessing...
