Searching content in different languages
Till now, we've talked mostly in theory about language analysis, for example, handling multiple languages our data can consist, and things like that. This will now change as we will now discuss how we can handle multiple languages in our data.
Why we need to handle languages differently
As you already know that ElasticSearch allows us to choose different analyzers for our data, we can have our data divided into words 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 like cat and cats by only sending the cat word to ElasticSearch? This is where language analysis comes into play with stemming algorithms for different languages, which allow reducing the analyzed words into their...
