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Usually documents that mix multiple languages in a single field come from sources beyond your control, such as pages scraped from the web:
{ "body": "Page not found / Seite nicht gefunden / Page non trouvée" }
They are the most difficult type of multilingual document to handle correctly.
While you can simply use the standard analyzer on all fields, your documents
will be less searchable than if you had used an appropriate stemmer. But of
course, you can’t choose just one stemmer — stemmers are language specific.
Or rather, stemmers are language and script specific. As discussed in
[different-scripts], if every language uses a different script, then
stemmers can be combined.
Assuming that your mix of languages uses the same script such as Latin, then there are three choices available to you:
The Compact Language Detector mentioned in [identifying-language] can tell you which parts of the document are in which language. You can split the text up based on language and use the same approach as was used in [one-lang-fields].
If you primarily deal with a limited number of languages, then you could use multi-fields to analyze the text once per language:
PUT /movies
{
"mappings": {
"title": {
"properties": {
"title": { (1)
"type": "string",
"fields": {
"de": { (2)
"type": "string",
"analyzer": "german"
},
"en": { (2)
"type": "string",
"analyzer": "english"
},
"fr": { (2)
"type": "string",
"analyzer": "french"
},
"es": { (2)
"type": "string",
"analyzer": "spanish"
}
}
}
}
}
}
}
The main title field uses the standard analyzer.
Each sub-field applies a different language analyzer
to the text in the title field.
You could just index all words as n-grams, using the same approach as described in [ngrams-compound-words]. Most inflections involve adding a suffix (or in some languages, a prefix) to a word, so by breaking each word down into n-grams, you have a good chance of matching words that are similar, but not exactly the same. This can be combined with the analyze-multiple- times approach to provide a catch-all field for unsupported languages:
PUT /movies
{
"settings": {
"analysis": {...} (1)
},
"mappings": {
"title": {
"properties": {
"title": {
"type": "string",
"fields": {
"de": {
"type": "string",
"analyzer": "german"
},
"en": {
"type": "string",
"analyzer": "english"
},
"fr": {
"type": "string",
"analyzer": "french"
},
"es": {
"type": "string",
"analyzer": "spanish"
},
"general": { (2)
"type": "string",
"analyzer": "trigrams"
}
}
}
}
}
}
}
In the analysis section, we define the same trigrams
analyzer as described in [ngrams-compound-words].
The title.general field uses the trigrams analyzer
to index any language.
When querying the catch-all general field, you can use
minimum_should_match to reduce the number of low quality matches. It may
also be necessary to boost the other fields slightly more than the general
field, so that matches on the the main language fields are given more weight
than those on the general field:
GET /movies/movie/_search
{
"query": {
"multi_match": {
"query": "club de la lucha",
"fields": [ "title*^1.5", "title.general" ], (1)
"type": "most_fields",
"minimum_should_match": "75%" (2)
}
}
}
All title or title.* fields are given a slight boost over the
title.general field.
The minimum_should_match parameter reduces the number of low
quality matches returned, especially important for the title.general field.
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