Extending the dataset from chapter 6 with the full Wikipedia page for each settlement, this chapter exemplifies natural language processing techniques to work with textual data. Textual problems are a domain that involves large number of correlated features, with feature frequencies strongly biased by a power law. Such behaviour is very common for many naturally occurring phenomena besides text. The very nature of dealing with sequences means this domain also involves variable length feature vectors.A central theme in this chapter is context and how to supply it within the enhanced feature vector to increase the signal-to-ratio available to the ML. As many times the target information (population) appears within the target page (as high as 53% of the cases in exact form), this problem is closely related to the NLP problem of Information Extraction. The chapter showcases different ways to approach the problem using words-as-features in the bag-of-words paradigm, then proceeding to do heavy feature selection using mutual information, stemming, modelling context with bigrams and skip-bigrams. More advanced topics include feature hashing and embeddings using word2vec.