Language Identification

Language identification is perhaps the simplest task in Natural Language Processing. It is the task of automatically determining which language a piece of text is written in.

It’s very simple to implement, and usually very fast to run. You might have come across it when you use an online translator and it automatically detects the source language.

There are many ways to solve this problem.

• Character / byte frequency models
• N-gram models
• fastText
• Byte-level transformer models

Let’s go over some of these methods.

Character / byte frequency models

1. Iterate through the text, examining each character or byte.
2. Count the occurrences of each unique character or byte.
3. Calculate the frequency of each character or byte by dividing its count by the total number of characters or bytes in the text.
4. Compare the resulting frequency profile to reference frequency profiles for different languages (often precomputed from large corpora) using a distance metric (such as cosine similarity or Euclidean distance).
5. Identify the language whose frequency profile is most similar to the one computed from the input text.

This method works well for many languages, especially when the input text is reasonably long, since different languages have distinctive character or byte distributions. However, it may be less reliable for very short texts, or for languages that use the same alphabet and have similar frequency distributions.

Datasets

• CommonLID

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