The built-in language analyzers are available globally and don’t need to be configured before being used. They can be specified directly in the field mapping:

1. The title field will use the english analyzer instead of the default standard analyzer.

Of course, by passing text through the english analyzer, we lose information:

1. Emits tokens: i’m, happi, about, fox

We can’t tell if the document mentions one fox or many foxes; the word not is a stopword and is removed, so we can’t tell whether the document is happy about foxes or not. By using the english analyzer, we have increased recall as we can match more loosely, but we have reduced our ability to rank documents accurately.

To get the best of both worlds, we can use multi-fields to index the title field twice: once with the english analyzer and once with the standard analyzer:

1. The main title field uses the standard analyzer.

2. The title.english sub-field uses the english analyzer.

With this mapping in place, we can index some test documents to demonstrate how to use both fields at query time:

1. Use the most_fields query type to match the same text in as many fields as possible.

Even though neither of our documents contain the word foxes, both documents are returned as results thanks to the word stemming on the title.english field. The second document is ranked as more relevant, because the word not matches on the title field.