Natural Language Processing - Tokenization (NLP Zero to Hero - Part 1)

Summary

This video script introduces the concept of tokenization in natural language processing (NLP) using TensorFlow, aimed at beginners. It explains how words can be represented numerically for a computer to process, using ASCII encoding and word-based encoding to illustrate the process. The script demonstrates encoding sentences into a sequence of numbers, showing similarity between sentences with shared words. It also covers using TensorFlow Keras' tokenizer API in Python to tokenize text, manage exceptions, and prepare data for neural network processing, with an invitation to experiment with the provided code in Colab.

Takeaways

• 📚 The series is aimed at beginners in AI and ML, teaching NLP concepts from first principles.
• 🔤 The script introduces the concept of word representation for a computer using tokenization.
• 🔢 Words can be represented by numbers using encoding schemes like ASCII, but this can be problematic for understanding word sentiment due to letter order.
• 📝 It's easier to encode words rather than letters, which helps in training a neural network to understand their meaning.
• 🐶 The example 'I love my dog' is used to illustrate how sentences can be encoded into a sequence of numbers.
• 🔑 The 'num_words' parameter in the tokenizer API limits the maximum number of words to keep, useful for large datasets.
• 📖 The tokenizer fits to the text, creating a word index that maps words to tokens.
• 👀 The tokenizer can handle exceptions, such as punctuation, without creating new tokens for them.
• 💻 The script provides a code example using TensorFlow Keras to demonstrate the tokenization process.
• 🔄 The tokenized words are essential for preparing data in a format suitable for neural network processing.
• 🔑 The next step after tokenization is to represent sentences as sequences of numbers for neural network training.

Q & A

What is the main topic of the video series?

The main topic of the video series is teaching natural language processing (NLP) using TensorFlow, starting from basic concepts for those who are not experts in AI or ML.

What is the process called that represents words in a way a computer can process them?

The process is called tokenization, which involves representing words in a numerical form that a computer can understand.

Why is it difficult for a computer to understand sentiment just by the letters in a word?

It's difficult because the same letters can form different words with different sentiments, such as 'listen' and 'silent', which have the same letters but different meanings.

What is an example of a word encoding scheme?

An example of a word encoding scheme is ASCII, which represents letters with specific numbers.

How does the script suggest we should represent sentences for easier processing by a computer?

The script suggests encoding words in a sentence rather than individual letters, assigning each unique word a numerical value.

What is the purpose of the 'num_words' parameter in the tokenizer object?

The 'num_words' parameter is used to specify the maximum number of words to keep when tokenizing text, allowing the tokenizer to focus on the most frequent words.

How does the tokenizer handle exceptions like punctuation?

The tokenizer is smart enough to ignore punctuation and not create new tokens for words followed by punctuation marks, treating them as the same word without the punctuation.

What is the result of the tokenizer's 'word index' property?

The 'word index' property of the tokenizer results in a dictionary where the key is the word and the value is the assigned numerical token for that word.

What is the next step after tokenization in preparing data for a neural network?

After tokenization, the next step is to represent sentences as sequences of numbers in the correct order, making the data ready for processing by a neural network.

Where can viewers find the code used in the video to experiment with tokenization?

Viewers can find the code in a Colab notebook linked in the video description, where they can experiment with the tokenization process.

What will be covered in the next episode of the series?

The next episode will cover tools for managing the sequencing of tokenized words, which is necessary for neural network processing.

Outlines

00:00

📘 Introduction to NLP and Tokenization

Laurence Moroney introduces the video series on Natural Language Processing (NLP) with TensorFlow, designed for beginners. He explains the concept of tokenization, which is the process of representing words in a way that computers can understand. The video discusses the limitations of ASCII encoding and the advantages of word encoding, which involves assigning numbers to words rather than individual letters. An example of encoding sentences is given, showing how similar sentences can be represented with a sequence of numbers, highlighting the potential for capturing semantic similarities. The video also includes a brief introduction to using TensorFlow Keras for tokenization with Python, mentioning the use of a tokenizer object and the 'num_words' parameter to handle large text datasets.

Keywords

💡Natural Language Processing (NLP)

Natural Language Processing is a subfield of artificial intelligence that focuses on the interaction between computers and human language. It enables machines to understand, interpret, and generate human language in a way that is both meaningful and useful. In the context of the video, NLP is the overarching theme, as the series aims to teach viewers how to process and understand human language using TensorFlow, starting from the basics.

💡Tokenization

Tokenization is the process of converting a sequence of characters, such as a sentence, into a sequence of tokens, which are the atomic units of language, typically words or phrases. In the video, tokenization is introduced as a crucial step in preparing text data for machine learning models. The script demonstrates how words are converted into numerical tokens, allowing a computer to process and understand the meaning of words in a sentence.

💡ASCII Encoding

ASCII encoding is a character encoding standard for electronic communication, which represents text in computers, telecommunications equipment, and other devices. It assigns a unique number for each character. In the script, ASCII encoding is used as an example to show how letters of the alphabet can be represented by numbers, which is a fundamental concept in converting words into a format that computers can process.

💡Neural Network

A neural network is a set of algorithms modeled loosely after the human brain that are designed to recognize patterns. They are a core component of deep learning, a subset of machine learning. The video mentions training a neural network to understand the meaning of words, indicating that once words are tokenized, they can be fed into a neural network to learn and make predictions based on language patterns.

💡TensorFlow

TensorFlow is an open-source software library for dataflow and differentiable programming across a range of tasks, and it is primarily used for machine learning and deep learning applications. It is highlighted in the video as the tool that will be used to teach viewers how to implement NLP techniques, starting with tokenization.

💡Tokenizer

A tokenizer is a tool or function that divides text into its component tokens, usually words or phrases. In the context of the video, the tokenizer is a specific API from TensorFlow Keras used to convert sentences into a numerical format that can be understood by a neural network. The script provides an example of creating a tokenizer object and using it to encode sentences.

💡Num_words Parameter

The 'num_words' parameter in the context of the tokenizer is a setting that specifies the maximum number of words to keep when tokenizing text. This is useful for managing the size of the vocabulary, especially when dealing with large datasets. The script mentions this parameter as a way to select only the most frequent words for training the model, thus simplifying the learning process.

💡Word Index

The word index is a dictionary that maps each unique word to a unique numerical identifier after tokenization. It serves as a lookup table for converting words into their corresponding tokens. In the script, the word index is accessed through the tokenizer's 'word_index' property, which is used to demonstrate how words are assigned numerical values.

💡Exception Handling

Exception handling is a programming technique used to manage errors and other exceptional conditions that occur during the execution of a program. In the video, the tokenizer's ability to handle exceptions, such as punctuation attached to words without creating new tokens, is mentioned as a feature that simplifies the tokenization process and helps maintain consistency in the data.

💡Sequence

In the context of NLP and machine learning, a sequence refers to an ordered list of elements, such as a series of tokens representing a sentence. The script mentions that after tokenization, the next step is to represent sentences as sequences of numbers, which is essential for feeding the data into a neural network for processing or generating text.

💡Colab

Colab is a free Jupyter notebook environment that requires no setup and runs entirely in the cloud. It is used for writing and executing Python code. The video script encourages viewers to try out the provided code in Colab, which suggests that the platform is a recommended tool for experimenting with TensorFlow and practicing the concepts taught in the series.

Highlights

Introduction to a series on Zero to Hero for natural language processing using TensorFlow.

Reassurance for beginners that the course will teach NLP concepts from first principles.

Explanation of word representation for computer processing through tokenization.

ASCII encoding scheme used to represent letters by numbers.

Challenge of encoding words by letters due to different orders in words like 'listen' and 'silent'.

Proposed solution to encode words instead of letters for better sentiment understanding.

Example of encoding the sentence 'I love my dog' with word numbers.

Demonstration of encoding similarities between sentences 'I love my dog' and 'I love my cat'.

Introduction to the process of tokenization with Python code examples.

Use of TensorFlow Keras to obtain tokenization APIs.

Explanation of the 'num_words' parameter for limiting the number of words in tokenization.

Fitting the tokenizer to text and accessing the word index property.

Tokenizer's ability to handle exceptions like punctuation without creating new tokens.

Invitation to try the provided code in Colab for hands-on experience.

Discussion on the next steps after tokenization for neural network training.

Teaser for the next episode focusing on managing sentence sequencing for neural network input.

Call to action to subscribe for further updates on the series.

Transcripts

LAURENCE MORONEY: Hi, and welcome to this series on Zero

to Hero for natural language processing using TensorFlow.

If you're not an expert on AI or ML, don't worry.

We're taking the concepts of NLP and teaching them

from first principles.

In this first lesson, we'll talk about how to represent words

in a way that a computer can process them,

with a view to later training a neural network that

can understand their meaning.

This process is called tokenization.

So let's take a look.

Consider the word "listen," as you can see here.

It's made up of a sequence of letters.

These letters can be represented by numbers

using an encoding scheme.

A popular one called ASCII has these letters represented

by these numbers.

This bunch of numbers can then represent the word listen.

But the word silent has the same letters, and thus

the same numbers, just in a different order.

So it makes it hard for us to understand sentiment of a word

just by the letters in it.

So it might be easier, instead of encoding letters,

to encode words.

Consider the sentence I love my dog.

So what would happen if we start encoding

the words in this sentence instead

of the letters in each word?

So, for example, the word "I" could be one,

and then the sentence "I love my dog" could be 1, 2, 3, 4.

Now, if I take another sentence, for example, "I love my cat,"

how would we encode it?

Now we see "I love my" has already been given 1, 2, 3,

so all I need to do is encode "cat."

I'll give that the number 5.

And now, if we look at the two sentences,

they are 1, 2, 3, 4 and 1, 2, 3, 5,

which already show some form of similarity between them.

And it's a similarity you would expect,

because they're both about loving a pet.

Given this method of encoding sentences into numbers,

now let's take a look at some code to achieve this for us.

This process, as I mentioned before, is called tokenization,

and there's an API for that.

We'll look at how to use it with Python.

So here's your first look at some code

to tokenize these sentences.

Let's go through it line by line.

First of all, we'll need the tokenize our APIs,

and we can get these from TensorFlow Keras like this.

We can represent our sentences as a Python array

of strings like this.

It's simply the "I love my dog" and "I love my cat"

that we saw earlier.

Now the fun begins.

I can create an instance of a tokenizer object.

The num_words parameter is the maximum number

of words to keep.

So instead of, for example, just these two sentences,

imagine if we had hundreds of books to tokenize,

but we just want the most frequent

100 words in all of that.

This would automatically do that for us

when we do the next step, and that's

to tell the tokenizer to go through all the text

and then fit itself to them like this.

The full list of words is available as the tokenizer's

word index property.

So we can take a look at it like this

and then simply print it out.

The result will be this dictionary showing the key

being the word and the value being the token for that word.

So for example, my has a value of 3.

The tokenizer is also smart enough

to catch some exceptions.

So for example, if we updated our sentences to this

by adding a third sentence, noting that "dog" here

is followed by an exclamation mark,

the nice thing is that the tokenizer

is smart enough to spot this and not create a new token.

It's just "dog."

And you can see the results here.

There's no token for "dog exclamation,"

but there is one for "dog."

And there is also a new token for the word "you."

If you want to try this out for yourself,

I've put the code in the Colab here.

Take it for a spin and experiment.

You've now seen how words can be tokenized,

and the tools in TensorFlow that handle

that tokenization for you.

Now that your words are represented

by numbers like this, you'll next

need to represent your sentences by sequences of numbers

in the correct order.

You'll then have data ready for processing by a neural network

to understand or maybe even generate new text.

You'll see the tools that you can

use to manage this sequencing in the next episode,

so don't forget to hit that subscribe button.

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