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In this article, we are going to see how we can easily integrate a Language Detection engine in your project and how to choose and access the right engine according to your data.
Language detection is the task of automatically detecting the language(s) present in a document based on the content of the document. Using a language detection engine, you can obtain the most likely language for a piece of input text, or a set of possible language candidates with their associated probabilities.
Language detection predates computational methods — the earliest interest in the area was motivated by the needs of translators, and simple manual methods were developed to quickly identify documents in specific languages. The earliest known work to describe a functional Language detection program for text is by Mustonen in 1965, who used multiple discriminant analysis to teach a computer how to distinguish between English, Swedish and Finnish.
In the early 1970s, Nakamura considered the problem of automatic Language detection. His language identifier was able to distinguish between 25 languages written with the Latin alphabet. As features, the method used the occurrence rates of characters and words in each language.
The highest-cited early work on automatic language detection is Cavnar and Trenkle in1994. Cavnar and Trenkle method builds up per-document and per-language profiles, and classifies a document according to which language profile it is most similar to, using a rank-order similarity metric.
You can use Language Detection in numerous fields, here are some examples of common use cases:
When you need a Language Detection engine, you have 2 options:
The only way you have to select the right provider is to benchmark different providers’ engines with your data and choose the best OR combine different providers’ engines results. You can also compare prices if the price is one of your priorities, as well as you can do for rapidity.
This method is the best in terms of performance and optimization but it presents many inconveniences:
Here is where Eden AI becomes very useful. You just have to subscribe and create an Eden AI account, and you have access to many providers engines for many technologies including Language Detection. The platform allows you to benchmark and visualize results from different engines, and also allows you to have centralized cost for the use of different providers.
Eden AI provides the same easy to use API with the same documentation for every technology. You can use the Eden AI API to call Language Detection engines with a provider as a simple parameter. With only a few lines, you can set up your project in production.
There are numerous Language Detection engines available on the market: it’s impossible to know all of them, to know those who provide good performance. The best way you have to integrate Language Detection technology is the multi-cloud approach that guarantees you to reach the best performance and prices depending on your data and project. This approach seems to be complex but we simplify this for you with Eden AI which centralizes best providers APIs.
In this article, we explain how the mapping between the input language and the languages supported by the providers is performed to facilitate access to one of our AI engines.