Next topic is part of speech tagging.
So what is here, the tagging me?
Let's go to this linguistic feature of this spacy and signing some kind of pact to individual bogon.
And those tax here are like a known pronoun, adverb, adjective.
So those type of English grammar, when you assign me to some token because English is like an Merriman's
exception language and all those human languages are all natural languages are all exceptions exist.
So the same token might behave like an adverb.
Sometimes that might be Lycan noun sometimes.
So that is the beauty here, that once you assign it to some particular tag, you will be able to read
those particular token differently.
When you go further, further analysis of your tax.
So Spacey has already in Belka, those part of speech tagging got implemented.
For illustration purposes, I kept text like people is looking at buying UK stock for one billion dollar.
Let's load it and let's load our simple English small size model.
So first, Todd Bridges tried to connect it in his lies and allocate some space to.
Now, once we apply this NLB function, let's try those quartos, so let me remove this one.
Let's say not.
And I'm just going to supply Presswood.
And let's see what every single token so Kocan in, let's say.
OK, let's think.
Can not, let's say, Texas.
So we get idea.
What other tokens and then we'll apply those post tagging on each and every token.
Let's say Goken not cause underscored, and that is a one more like op ed underscore certain Tolkan
not tag.
And this could and if you want a detailed explanation about each and every dose tag.
You can use like us facey not explain dog tag from the school.
So it will give you a detailed description related to each individual.
Pocan has been assigned to other Tolkan.
And you can see how police were known and ENPI and its description will be known proper, singular,
this ease's LAGOONA.
He x auxillary well, I guess per person, singular presence.
So it's very much detail.
Englis Grammatical stuff.
Nahlah Like a symbol and it's a kind of currency.
One is no kind of cardinal number billion.
Billion is also no cardinal.
No.
So that is how beautiful and intelligently space he has identified each and every token UK like on a
proper noun, proper singular noun.
Startup is also a startup is a noun.
All right.
So that is how you can get the horse tagging.
Now, if you want to get how many of them are Liko now and how many of them are a proper noun, if you
want to do such a kind of analysis, you can just simply use dot dot count by metter.
And here you can pass spacy.
Not.
Bopp's.
Mattos.
What was?
Antiquities display.
We'll be able to see it as displayed like a eighty five plus two time eighty seven accords one time.
But what is this?
Eighty five.
Seven ninety two.
So let us a little what.
So far he tanah well in.
So I'm just going to agree or item.
And let me.
This place was he and red and then we'll explain the meaning of all of them.
I guess it's items.
Yes, it's an item.
Suppose.
What is the meaning of 96?
And that occurs to time if you want to grab it.
We can use like a dog dog walker.
And yet I can pass like an key.
And we'll get much more ideas.
How to apply tax also, because then it will just display the full object name only.
All right, so PR., of course, to name a U.S. subsidiary, because when buying vlachos two times.
So it goes kind of analysis also very much helpful.
How many entities are there?
How many votes are there?
How many names are there?
How many symbols are there?
So where our document is much more oriented, let's say if we are dealing with some financial document.
Maybe you'll encounter more and more like a money related stuff.
So more and more symbols like our dollars sign other countries currency.
You'll be able to see there are more numbers you will encounter.
All right.
So that is how it will extract those kind of features.
Now, suppose this part of speech tagging, you want to display in some Raziel manner, we can use it
from Spenceley.
There will be a one song shine like a display.
And let Sironi.
Now, we already have a dark object.
So we are going to use this display, see?
Not Wrangler, which will display all those part of speech training assigned to each and every individual
token in some manner.
So first argument requires locks.
And then we require Stice.
So docs will be over.
No object.
What we accreted.
Let's make it style.
Or style.
Just let us make it.
BP only let's make Jupiter is equal.
And let's run it.
And it's very big, actually.
Let's make it a little smaller because our sentence is not that much big.
So Apple is better known than Easy's auxiliary.
Looking is like an evil mind is also well, and there is a connection that something is looking for
something.
So those kind of connection also it has festivals.
Let's make it a little smaller.
So for that.
We can pass on options like.
Let's say distance.
Let's make this 10 some handshake.
And let me executed a search, it's a little smaller.
Let's make it even smaller.
80.
All right.
So you can see very beautiful results, all those part of speech tagging to every single tokenism really
well in the public document object.
All right.
So that is all about the part of speech tagging.
See you in the next video.