If you have to deal with only a single language, count yourself lucky. Finding the right strategy for handling documents written in several languages can be challenging.
Multilingual documents come in three main varieties:
One predominant language per document, which may contain snippets from other languages (See [one-lang-docs].)
One predominant language per field, which may contain snippets from other languages (See [one-lang-fields].)
A mixture of languages per field (See [mixed-lang-fields].)
The goal, although not always achievable, should be to keep languages separate. Mixing languages in the same inverted index can be problematic.
The stemming rules for German are different from those for English, French, Swedish, and so on. Applying the same stemming rules to different languages will result in some words being stemmed correctly, some incorrectly, and some not being stemmed at all. It may even result in words from different languages with different meanings being stemmed to the same root word, conflating their meanings and producing confusing search results for the user.
Applying multiple stemmers in turn to the same text is likely to result in rubbish, as the next stemmer may try to stem an already stemmed word, compounding the problem.
In [relevance-intro], we explained that the more frequently a term appears in a collection of documents, the less weight that term has. For accurate relevance calculations, you need accurate term-frequency statistics.
A short snippet of German appearing in predominantly English text would give more weight to the German words, given that they are relatively uncommon. But mix those with documents that are predominantly German, and the short German snippets now have much less weight.
It is not sufficient just to think about your documents, though. You also need
to think about how your users will query those documents. Often you will be able
to identify the main language of the user either from the language of that user’s chosen
interface (for example, mysite.de versus mysite.fr) or from the
accept-language
HTTP header from the user’s browser.
User searches also come in three main varieties:
Users search for words in their main language.
Users search for words in a different language, but expect results in their main language.
Users search for words in a different language, and expect results in that language (for example, a bilingual person, or a foreign visitor in a web cafe).
Depending on the type of data that you are searching, it may be appropriate to return results in a single language (for example, a user searching for products on the Spanish version of the website) or to combine results in the identified main language of the user with results from other languages.
Usually, it makes sense to give preference to the user’s language. An English-speaking user searching the Web for ``deja vu'' would probably prefer to see the English Wikipedia page rather than the French Wikipedia page.
You may already know the language of your documents. Perhaps your documents are created within your organization and translated into a list of predefined languages. Human pre-identification is probably the most reliable method of classifying language correctly.
Perhaps, though, your documents come from an external source without any language classification, or possibly with incorrect classification. In these cases, you need to use a heuristic to identify the predominant language. Fortunately, libraries are available in several languages to help with this problem.
Of particular note is the chromium-compact-language-detector library from Mike McCandless, which uses the open source (Apache License 2.0) Compact Language Detector (CLD) from Google. It is small, fast, and accurate, and can detect 160+ languages from as little as two sentences. It can even detect multiple languages within a single block of text. Bindings exist for several languages including Python, Perl, JavaScript, PHP, C#/.NET, and R.
Identifying the language of the user’s search request is not quite as simple.
The CLD is designed for text that is at least 200 characters in length.
Shorter amounts of text, such as search keywords, produce much less accurate
results. In these cases, it may be preferable to take simple heuristics into
account such as the country of origin, the user’s selected language, and the
HTTP accept-language headers.