Hello, everyone, and welcome back.
So the new topic, which we are going to learn in this accent is a word M80, and it is one of the very
important and crucial step, you know, any kind of natural language processing, delicate task.
So what I'm reading is you can see like it has complete political slides.
There's an LP field.
So what exactly avoid embedding is?
Let's see if we get to straight to the computer.
And if you tell computer to match character by character, I mean computer can easily do it.
And computer can do very much at a faster rate.
But suppose if you're searching for, let's say, massee on a Google Web search and you'll get the results
related to football, also, you will get results related to internal Loiselle.
So why such things happen as a human?
We know that this message is related to football lives that aren't always related to football.
And this three towns are interconnected.
But that doesn't signifies with a complete string matching because these two strings are completely
different.
So how computer can understand that these three things are related to each other, even if there is
no magic between them.
This football itself is a in terms of computer representation, it's a completely different thing compared
to what Partovi, Masti and other Nilo represented in a computer.
So that string representation is not sufficient.
While comparing this type of things, but as we see in Google Web site, it is quite smart enough.
And they're using internally this voyle imaging technique so that we get the idea that when you search
for Masoli, you will get the results related to football.
And and so let's take one more example.
Let's say Happel is a tasty fruit.
Now, in this particular sentence, how can Computer understand that apple is a fruit which can be eaten?
But that is not an organization.
So that is the kind of intelligence this void embedding will bring into our natural language processing.
Martin.
Well, the meeting is all about understanding about your tax, the semantic represent and exist between
individual votes, and let's try to formally define it.
So the word I'm reading is all about the representation of your word, which captures the meaning of
immediate word.
The semantic relationship exists between the different types.
So what?
And the same word which is being used in a different kind of contexts.
And all this thing, we are going to get implemented by this word embedding technique, which is nothing
but a numerical representation of your text.
So once you convert this text into kind of numerical representation, you can have a comparison between
them.
You can define some sort of distance measure criteria.
How closely these two words are related to each other or how far they're situated in a full gamut of
Englis Dictionary.
But why does what I'm reading is required?
Now, if you observe that many of the machine learning algorithm are almost all machine learning and
deep learning architecture, I am indeed just cannot process the text directly in that raw form.
So my idea here is to convert this plane taxied to kind of raw numbers and once we convert it a raw
numbers so we can apply a different machine learning algorithms like a classification variation and
listening because they require such a kind of number as input.
They just cannot understand text statically as the input and void.
Embedding plays a very important, vital role for converting this vote to a kind of text.
Now, what are the different types of war and embedding techniques?
So at a broad level, you can divide this technique into two different media categories.
So like a frequency base, embedding and undermine is up Predix and Baizhang.
Now, our main focus here will be to learn about this prediction base embedding.
And we won't go into much detail about the critical part, but mainly Vokes based on the neural network
architecture.
So one is a CB or W that will be a continuous Baggot Ford model and undermining this.
Keep Cramerton.
So here what we are trying to do based on the context of war or the word which is surrounded by some
particular war, we are trying to build some neural network model and those neural network model will
come up with a vector representation of your any of the words that will be kind of very best representation
you can see compared to this frequency base embedding like a contractor, DFI of Corkins.
So mostly both of this countercurrent mean contractors and T.F. idea.
Both of them behave a little seen in our text classification projects where we are trying to find the
same numerical representation based on the frequency that how many times some particular work.
So that will be a contractor.
How many times the combination of two or three or multiple words occur together.
So that will be idea behind this co-occurrence vector and a little bit better.
An advanced technique like a PEF idea.
So that will count further how many times some particular Kaarina document.
And another component as the IDF like inverse document frequency stack.
How many times some particular volke across all the documents.
So these are all basic frequency based embedding technique and under cites is prediction based modeling
technique.
Let me give you a little bit more detail.
So let me go to the call up and then in the next video, we will see how to implement VITTA, one of
the NLB library, Jency Predix and base embedding technique.
So for better understanding purposes, I created this KALEV file.
And there are three examples I have given, like how you can represent the text into a kind of number.
So let's have one heart encoding.
So that will be kind of bag of words kind of model.
So that says that, let's say rehabber, just the one sentence like a dog gets sat on the mat.
So totally velho how unique words are available like doe cat sat on hand.
So wherever that particular word occurs, the next position of that particular word will be given the
value one meaning all values will be zero.
So that is Koller's of one order encoding.
Now for the what the vector will be zero zero zero zero one because at this particular date only occupies
one remaining.
All values will be zero.
But now this approach is quite efficient because whatever than one hardcoded vector you will get, that
is a very sparse.
So let's see if you can just imagine the total words in a dictionary like a 10000 word dictionary.
So in every single vector you will get almost ninety nine point ninety nine percent element will be
zero.
Only one place there will be a value one.
So this approach will be quite inefficient.
Let's go.
We done under outputs like a code, each word with some unique number.
So let's take a same example.
Let's say the cats act on a map.
Now we will give one single number to every single voice.
So that will be assigned to one and two will be assigned to set simply some numbers will be assigned
to every single unique one.
Now, this particular approach is even more efficient, but that is better compared to this sparse vector
approach.
Now here the main problem is that when you convert this type of things into this, this is as even a
label.
Bayes indexing.
Also.
Let's say no has been presented with one and this two has been represented.
I mean, said has been represented by 10 and 12.
Now, when we supply this thing to the machine learning algorithm, the algorithm will understand that
set is better than that.
But that is not a case, actually.
That is not a case, because now once we convert it into two number, it should also make sense how
something is not better than other in terms of text.
But when we convert it to number of automate, it will be something like that.
And that control makes.
Well, I saw it last one, an important one is a word emerging so you can read about things here.
And I just want to show you that compared to earlier to approach this word embedding approach will try
to give us some fixed line rector.
So let's say in our case, the vector land will be, let's say, for Davidson.
So care has been represented in such a kind of four day image that Matt has been represented by this
phone number.
And on has been represented by this phone number.
So this with a four dimensional embedding will create for every single voice or I would say unique words.
Really?
Well, you know, what a dictionary.
And that is what the word Embling is and how we are going to create this thing based on the neural network.
All right.
So see you in the next video.
We'll get my hands on practical tech, how we can create our own war and maybe based on some simple
dataset.
All right.
See you in the next video.