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Language detection

What is language detection?

In natural language processing, language detection is the determining of which natural language given content is in. Computational approaches to this problem view it as a special case of text categorization, solved with various statistical methods.

Most NLP applications tend to be language-specific and therefore require monolingual data. To build an application in your target language, you may need to apply a preprocessing technique that filters out text written in non-target languages. This requires proper identification of the language of each input example. 

Applications of Language Detection

In Natural Language Processing (NLP), one may need to work with data sets that contain documents in various languages. Many NLP algorithms only work with specific languages because the training data is usually in a single language. It can be a valuable time saver to determine which language your data set is in before running more algorithms on it.

An example of a Language Detection algorithm lies in the web search arena. A web crawler will hit pages that are potentially written in one of many different languages. If this data is to be used by a search engine, the results will be most helpful to the end-user if the language used in the search is the same as the results. Thus, you can quickly see how a web developer who must work with content in multiple languages would want to implement language detection as a search functionality.

Spam filtering services that support multiple languages must identify the language that emails, online comments, and other input are written in before applying true spam filtering algorithms. Without such detection, content originating from specific countries, regions, or areas suspected of generating spam cannot be adequately eliminated from online platforms.

How Language Detection works

Language classifications rely upon using a primer of specialized text called a 'corpus.' There is one corpus for each language the algorithm can identify. In summary, the input text is compared to each corpus, and pattern matching is used to identify the strongest correlation to a corpus.

Because there are so many potential words to profile in every language, computer scientists use algorithms called 'profiling algorithms' to create a subset of words for each language to be used for the corpus. The most common strategy is to choose very common words. For example, in English, we might choose words like "the," "and," "of," and "or."

This approach works well when the input data is relatively lengthy. The shorter the phrase in the input text, the less likely these common words appear, and the less likely the algorithm will classify correctly. In fact, some languages don't have spaces between written words, making such isolation impossible.

Facing this problem, researchers tried to use character sets generally, rather than relying on them being split into words. Even if the words have spaces between them, depending on the natural words alone often causes problems when analyzing short phrases.

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Language detection

October 14, 2020

Table of contents

Key takeawaysCollaboration platforms are essential to the new way of workingEmployees prefer engati over emailEmployees play a growing part in software purchasing decisionsThe future of work is collaborativeMethodology

What is language detection?

In natural language processing, language detection is the determining of which natural language given content is in. Computational approaches to this problem view it as a special case of text categorization, solved with various statistical methods.

Most NLP applications tend to be language-specific and therefore require monolingual data. To build an application in your target language, you may need to apply a preprocessing technique that filters out text written in non-target languages. This requires proper identification of the language of each input example. 

Applications of Language Detection

In Natural Language Processing (NLP), one may need to work with data sets that contain documents in various languages. Many NLP algorithms only work with specific languages because the training data is usually in a single language. It can be a valuable time saver to determine which language your data set is in before running more algorithms on it.

An example of a Language Detection algorithm lies in the web search arena. A web crawler will hit pages that are potentially written in one of many different languages. If this data is to be used by a search engine, the results will be most helpful to the end-user if the language used in the search is the same as the results. Thus, you can quickly see how a web developer who must work with content in multiple languages would want to implement language detection as a search functionality.

Spam filtering services that support multiple languages must identify the language that emails, online comments, and other input are written in before applying true spam filtering algorithms. Without such detection, content originating from specific countries, regions, or areas suspected of generating spam cannot be adequately eliminated from online platforms.

How Language Detection works

Language classifications rely upon using a primer of specialized text called a 'corpus.' There is one corpus for each language the algorithm can identify. In summary, the input text is compared to each corpus, and pattern matching is used to identify the strongest correlation to a corpus.

Because there are so many potential words to profile in every language, computer scientists use algorithms called 'profiling algorithms' to create a subset of words for each language to be used for the corpus. The most common strategy is to choose very common words. For example, in English, we might choose words like "the," "and," "of," and "or."

This approach works well when the input data is relatively lengthy. The shorter the phrase in the input text, the less likely these common words appear, and the less likely the algorithm will classify correctly. In fact, some languages don't have spaces between written words, making such isolation impossible.

Facing this problem, researchers tried to use character sets generally, rather than relying on them being split into words. Even if the words have spaces between them, depending on the natural words alone often causes problems when analyzing short phrases.

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