Welcome to an exciting journey into the world of natural language processing (NLP), where we’ll explore the wonders of tokenization. Whether you’re an AI enthusiast or just curious about the magic behind language processing, this series will guide you from zero to hero in NLP using TensorFlow.
Tokenization is the art of representing words in a way that computers can understand and process them. It’s like cracking the code of language, enabling computers to grasp the meaning behind each word. Imagine the possibilities!
Contents
Cracking the Code: From Letters to Numbers
Let’s start with a simple word: “listen.” At first glance, it’s just a sequence of letters. But to a computer, it’s a sequence of numbers. We can use an encoding scheme like ASCII to represent each letter with a corresponding number. Fascinating, isn’t it?
But here’s where things get tricky. The word “silent” has the same letters as “listen,” just in a different order. So if we rely solely on the letters, we might miss the true sentiment behind a word. Perhaps there’s a better way.
The Power of Words: Encoding Sentences
Instead of encoding letters, why not encode entire words? Let’s take a sentence as an example: “I love my dog.” If we assign a unique number to each word, we can represent the entire sentence as a sequence of numbers. In this case, “I” could be 1, “love” could be 2, “my” could be 3, and “dog” could be 4.
Now, let’s consider another sentence: “I love my cat.” Since we’ve already encoded “I love my” as 1, 2, 3, all we need to do is assign a number to “cat.” Let’s say 5. By comparing the encoded sequences of the two sentences (1, 2, 3, 4 and 1, 2, 3, 5), we can already identify some similarity between them. Both sentences revolve around the theme of loving a pet.
By encoding sentences into numbers, we unlock a world of possibilities. But how can we achieve this? Fear not, there’s an API for that!
Introducing Tokenization: The Path to Enlightenment
Tokenization is the process of converting sentences into sequences of numbers. And lucky for us, TensorFlow provides an API that simplifies this task. Let’s take a sneak peek at some Python code that does the magic of tokenization.
# Importing the necessary libraries
from tensorflow import keras
# Representing sentences as a Python array of strings
sentences = ["I love my dog", "I love my cat"]
# Creating an instance of the tokenizer object
tokenizer = keras.preprocessing.text.Tokenizer(num_words=100)
# Fitting the tokenizer to the text
tokenizer.fit_on_texts(sentences)
# Retrieving the word index
word_index = tokenizer.word_index
# Printing out the result
print(word_index)This code is just the tip of the iceberg. By specifying the maximum number of words (num_words) we want to keep, we can tokenize large volumes of text effortlessly. The tokenizer takes care of the rest, providing us with a dictionary that associates each word with its corresponding token. Now, that’s magical!
But tokenization doesn’t stop there. The tokenizer is intelligent enough to handle exceptions. For example, if we modify our sentences and add a third one, like “I love my dog!” with an exclamation mark, the tokenizer won’t treat it as a separate token. It recognizes that “dog” remains the same, while introducing a new token for the word “you.” The power of automation!
Embrace the Magic: Explore the Code
If you’re eager to dive into the wizardry of tokenization yourself, I’ve prepared a Colab notebook with the code for you to experiment with. Feel free to take it for a spin and unlock the secrets of language processing. The link to the notebook is Techal.
Congratulations! You’ve witnessed the enchantment of tokenization and the wonders of TensorFlow’s tools. But we’ve only scratched the surface. In the next episode, we’ll explore how to represent sentences as sequences of numbers in the correct order. Get ready to unleash the power of neural networks!
Don’t forget to hit that subscribe button, my dear friend. The journey has just begun, and I can’t wait to share more juicy secrets with you, my besties!
