Dealing with text data
We have already learned how to transform categorical features into numerical representations, either using label encoders, ordinal encoders, or one-hot encoding. However, what if we have fields containing long piece of text in our dataset? How are we supposed to provide a mathematical representation for them in order to properly feed ML algorithms? This is a common issue in natural language processing (NLP), a subfield of AI.
NLP models aim to extract knowledge from texts; for example, translating text between languages, identifying entities in a corpus of text (also known as Name Entity Recognition (NER)), classifying sentiments from a user review, and many other applications.
Important note
In Chapter 2, AWS Application Services for AI/ML, you learned about some AWS application services that apply NLP to their solutions, such as Amazon Translate and Amazon Comprehend. During the exam, you might be asked to think about the fastest or easiest way (with...