Unravel the Power of Machine Translation in NLP: A Comprehensive Guide with NLTK and Python 3

Machine Translation in NLP using python | Innovate Yourself


Greetings, aspiring Python enthusiasts! Today, we embark on an exciting journey into the realm of Natural Language Processing (NLP) with a focus on machine translation. We will delve into the intricate world of language processing, exploring how Python, NLTK (Natural Language Toolkit), and the powerful PyCharm IDE come together to make you a maestro in this field. So, buckle up and let’s embark on this thrilling ride!

Understanding Machine Translation

What is Machine Translation?

Machine translation is the art of automating the translation of text from one language to another using computational models. In NLP, it’s a fascinating application that finds its utility in various domains such as language learning, global communication, and content localization.

The Power of NLTK

NLTK, our trusty companion in this journey, is a robust Python library for processing and analyzing human language data. It provides a plethora of tools and resources for tasks like tokenization, stemming, tagging, parsing, and, of course, machine translation.

Setting Up the Stage: Installing NLTK and PyCharm

Before we dive into the world of machine translation, let’s ensure we have the necessary tools at our disposal. Fire up your PyCharm IDE and install NLTK by running:

pip install nltk
pip install googletrans==4.0.0-rc1

Now, let’s import NLTK in our Python script:

The Magic of Tokenization

Tokenization is the process of breaking down text into individual words or phrases, commonly known as tokens. NLTK provides powerful tools for this, making it a breeze to preprocess language data.

from nltk.tokenize import word_tokenize
import nltk
# Sample text
text = "Machine translation is revolutionizing global communication."

# Tokenizing the text
tokens = word_tokenize(text)

# Displaying the result
['Machine', 'translation', 'is', 'revolutionizing', 'global', 'communication', '.']

This snippet tokenizes our text, breaking it down into a list of words. Simple, right?

Translation in Action: English to French

Now, let’s add some flair by translating our English text into French. NLTK has built-in language translation modules, and for this example, we’ll use the ‘fr’ (French) module.

from googletrans import Translator

# Creating a Translator instance
translator = Translator()

# Translating English to French
french_translation = translator.translate(text, dest='fr')

# Displaying the result
La traduction automatique révolutionne la communication globale.

Voilà! Our English text has seamlessly transformed into French.

Elevating the Experience with Plots

Let’s visualize our translation prowess with some interactive plots. We’ll use matplotlib for this, so make sure to install it:

pip install matplotlib

Now, let’s visualize the word frequency distribution in both English and French texts.

import matplotlib.pyplot as plt

# English text word frequency
english_freq_dist = nltk.FreqDist(tokens)
english_freq_dist.plot(30, cumulative=False)
plt.title('Word Frequency Distribution - English')

# French text word frequency
french_tokens = word_tokenize(french_translation.text)
french_freq_dist = nltk.FreqDist(french_tokens)
french_freq_dist.plot(30, cumulative=False)
plt.title('Word Frequency Distribution - French')
  • Machine Translation in NLP using python | Innovate Yourself
  • Machine Translation in NLP using python | Innovate Yourself
  • Machine Translation in NLP using python | Innovate Yourself
  • Machine Translation in NLP using python | Innovate Yourself

These plots provide a captivating glimpse into the differences in word frequency between the two languages.

Going Deeper: Adding a Sample Dataset

To truly grasp the intricacies of machine translation, let’s work with a sample dataset. Consider a bilingual dataset containing English and French sentences. NLTK conveniently provides such datasets.

from nltk.corpus import europarl_raw

# Loading English-French parallel corpus
english_sentences = europarl_raw.english.raw().split('\n')[:5]
french_sentences = europarl_raw.french.raw().split('\n')[:5]

# Displaying the sample dataset
for eng, fr in zip(english_sentences, french_sentences):
    print(f"English: {eng}\nFrench: {fr}\n")

English: Resumption of the session I declare resumed the session of the European Parliament adjourned on Friday 17 December 1999 , and I would like once again to wish you a happy new year in the hope that you enjoyed a pleasant festive period .
French: Reprise de la session Je déclare reprise la session du Parlement européen qui avait été interrompue le vendredi 17 décembre dernier et je vous renouvelle tous mes vux en espérant que vous avez passé de bonnes vacances .

English: Although , as you will have seen , the dreaded ' millennium bug ' failed to materialise , still the people in a number of countries suffered a series of natural disasters that truly were dreadful .
French: Comme vous avez pu le constater , le grand " bogue de l' an 2000 " ne s' est pas produit .

English: You have requested a debate on this subject in the course of the next few days , during this part-session .
French: En revanche , les citoyens d' un certain nombre de nos pays ont été victimes de catastrophes naturelles qui ont vraiment été terribles .

English: In the meantime , I should like to observe a minute ' s silence , as a number of Members have requested , on behalf of all the victims concerned , particularly those of the terrible storms , in the various countries of the European Union .
French: Vous avez souhaité un débat à ce sujet dans les prochains jours , au cours de cette période de session .

This snippet loads a small portion of the Europarl parallel corpus, allowing us to work with actual bilingual data.

Crafting Your Expertise

As you continue on your journey to becoming a Python pro, don’t forget to experiment with different datasets, explore advanced NLP techniques, and stay curious. The world of machine translation in NLP is vast, and the more you explore, the more proficient you become.


In this guide, we’ve unveiled the captivating world of machine translation in NLP using NLTK and Python. Armed with the knowledge of tokenization, translation, and data visualization, you’re now well-equipped to conquer the intricacies of language processing.

Remember, the key to mastery lies in practice. So, fire up your PyCharm IDE, experiment with diverse datasets, and watch your Python prowess soar to new heights.

Also, check out our other playlist Rasa ChatbotInternet of thingsDockerPython ProgrammingMachine LearningNatural Language ProcessingMQTTTech NewsESP-IDF etc.
Become a member of our social family on youtube here.
Stay tuned and Happy Learning. ✌🏻😃
Happy coding, and may your NLP endeavors be both enlightening and rewarding! ❤️🔥🚀🛠️🏡💡

Leave a Reply