Till now, when discussing language analysis, we've talked mostly in theory. We didn't see an example regarding language analysis, handling multiple languages that our data can consist of, and so on. Now this will change, as we will discuss how we can handle data in multiple languages.
As you already know, Elasticsearch allows us to choose different analyzers for our data. We can have our data divided on the basis of whitespaces, have them lowercased, and so on. This can usually be done with the data regardless of the language—you should have the same tokenization on the basis of whitespaces for English, German, and Polish (that doesn't apply to Chinese, though). However, what if you want to find documents that contain words such as cat and cats by only sending the word cat to Elasticsearch? This is where language analysis comes into play with stemming algorithms for different languages, which allow the analyzed words to...