تدريب Shadowing: Natural Language Processing - Tokenization (NLP Zero to Hero - Part 1) - تعلم التحدث بالإنجليزية عبر الفيديو

Hi, and welcome to this series on Zero2Hero 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 words?

So for example, the word I could be one.

And then the sentence, I love my dog, could be one, two, three, four.

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 one, two, three.

So all I need to do is encode a cat.

I'll give that the number five.

And now if we look at the two sentences, they are one, two, three, four and one, two, three,

five, which already show some form of similarity between them.

And it's a similarity you'd 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 tokenizer 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 numWords 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 three.

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's also a new token for the word you.

If you want to try this out for yourself, I've put the code in a 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.

✨ فيديو موصى به

145 جمل

السيناريو

تخيل أنك في محادثة مع صديقك حول حيواناتك الأليفة. تتحدثان عن مدى حبكما للكلاب والقطط. هذا النوع من المحادثات اليومية يتطلب منكم استخدام عبارات طبيعية وتعبيرات تعكس مشاعركم. في الفيديو، تم تناول كيفية معالجة اللغة الطبيعية من خلال تناول الكلمات وطرق تمثيلها بشكل يجعل الكمبيوتر يفهمها. سنستفيد من هذا السياق لتطوير مهاراتنا في تحدث اللغة الإنجليزية.

المجموعات والكلمات المفيدة

I love my dog - أحب كلبي
I love my cat - أحب قطتي
My favorite pet - حيواني الأليف المفضل
Taking care of - العناية بـ
Playing with - اللعب مع

احرص على حفظ هذه العبارات واستخدامها في محادثاتك اليومية. ستساعدك في تحسين النطق باللغة الإنجليزية وتعبير مشاعرك بشكل أفضل.

تحدي الظل

تحدي اليوم هو أن تستمع إلى جزء من الفيديو وتقوم بتقليد ما تسمعه. حاول أن تركز على النطق والإيقاع. قم بتسجيل نفسك أثناء التقليد واستمع إلى تسجيلك بعد ذلك. هل يمكنك ملاحظة أية اختلافات في تحسين النطق باللغة الإنجليزية الخاص بك؟ استخدم كلمات مثل "love" و"dog" في جمل مختلفة وكررها عدة مرات. هذه طريقة ممتازة لـ تعلم الإنجليزية عبر الفيديو وتعزيز مهاراتك في shadowspeak.

القواعد في هذا الفيديو

أكثر التراكيب التي يستخدمها المتحدث، مع العبارات نفسها من الفيديو:

التركيب	في الفيديو
المبني للمجهول be + اسم المفعول — التركيز على ما يحدث لا على من يفعله	is called · can be represented · been given
المضارع التام have/has + اسم المفعول — حدث ماضٍ ما زال له أثر الآن	has already been given · I've put · You've now seen

ما هي تقنية التظليل الصوتي؟

التظليل الصوتي (Shadowing) تقنية تعلم لغة مدعومة علمياً، طُورت أصلاً لتدريب المترجمين الفوريين المحترفين. الطريقة بسيطة لكنها قوية: تستمع لصوت إنجليزي أصلي وتكرره فوراً بصوت عالٍ — كظل يتبع المتحدث بتأخير 1-2 ثانية. تُظهر الأبحاث تحسناً كبيراً في دقة النطق والتنغيم والإيقاع وربط الأصوات والاستماع والطلاقة.

تقنية الشادوينغ: اقرأ الدليل الكامل خطوة بخطوة →

تدريب Shadowing: Natural Language Processing - Tokenization (NLP Zero to Hero - Part 1) - تعلم التحدث بالإنجليزية عبر الفيديو

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السيناريو

المجموعات والكلمات المفيدة

تحدي الظل

القواعد في هذا الفيديو

ما هي تقنية التظليل الصوتي؟