Popular Python Libraries in NLP: Dealing with Language Detection, Translation & Beyond!

Popular Python Libraries in NLP: Dealing with Language Detection, Translation & Beyond!

The internet is flooded with articles and posts for detecting the language of texts and then translating it to any other language using Machine Learning or Deep Learning LSTM Models, transfer learning transformer Models like BERT, GPT-2, etc. and building a deep neural network for developing your own translation model. Popular Python Libraries in NLP: Dealing with Language Detection, Translation & Beyond!

The internet is flooded with articles and posts for detecting the language of texts and then translating it to any other language using Machine Learning or Deep Learning LSTM Models, transfer learning transformer Models like BERT, GPT-2, etc. and building a deep neural network for developing your own translation model.

But if you don’t want to write long codes and develop your own algorithms then there are certain python libraries which come handy to do these tasks on huge data and they work quite well mostly.

Following are few libraries which make your life easy for such tasks and are multi functional.

Google Translate:

This library comes with the name _Googletrans. _ Googletrans is a free and unlimited library which implemented Google Translate API to make calls to such methods like detect and translate. It is fast and reliable and uses same servers that translate.google.com uses.

Easy to install: “pip install googletrans

It supports multiple languages including some of the indian languages which can be listed by below python code.

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It detects multiple texts in different languages with the confidence of detection result which lies between 0.0 and 1.0 .

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Translating from one to other language is also quite simple as shown by below code.

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If we don’t give source language of the text, this library automatically tries to detect it and translates it to given destination language. If no destination language is given as shown in above sample code, then it tries to translate the given text to set language of the machine on which this runs. In this case it translated Korean to English, as my system’s default set language is English.

If you want to use translated text for your text pre-processing then use below sample code:

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So here, I have tried to translate German to Hindi. Isn’t it cool? Let’s move to next library.

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