Hello, everyone, and welcome back.
So the next step in Texas and exposes which we are going to learn in this video are the timing and limitation.
So limitation you can see like a little upgraded was born of this stemming for further analysis of your
tax, but not here.
Basically, stemming is so finding the root word of your original word.
So let's say we have words like, let's say playing, or maybe it will be a play or it will be a play.
So all those words, you can just simply convert it into your Rudaw like a play.
So that is called a stemming no limitations and is also exactly like an stemming only.
But the only basic difference between distending and a limitation is how many will you convert all your
work into your root or kind of stem word?
Not necessarily that in case of stemming the output.
What will be a part of your original dictionary or it will be a part of vocabulary.
So we will see some example and you will get a more idea.
What is the basic difference between stemming a limitation?
Whereas in case of limitation, whenever you convert your original void and find its lamma, whatever
the root word we will get, that is always be a part of vocabulary.
Now, the spacy library doesn't help any kind of stemming algorithm got implemented.
Instead of that, they have only limited implementation.
So what do we do in this video we'll use?
Let me just make it to the last line.
So playing play played.
Everything will be complete.
All right.
So for stemming the two algorithms are available, that is a part of this.
And LDK Library, not part of spacy library.
That will be a part of STEMMER.
And under one is a snowball steamer.
So let's say we help these words are available and for this work with the help of this analytical library,
author Stemmer and a Snowball Stemmer.
We are going to find it's the root word like run, right.
Not running.
Right.
So all this should convert to one base record.
So let's see what result we will get after applying this part timer.
Hannah Snowball Stemmer.
So first, let me put this in taking.
Next, let's import from Analytica, not stem.
Not water.
We are going to import
water it.
And that is on the Stamatis, that's all available.
That is a snowball stamp.
So it will be a from an LDK not.
STEM not.
Snowbowl, let's import Snow-White Stemmer.
Now let's create the object of MODOK class.
So it will be a part of Stemmer.
Let me Dupee in this code.
Stamler and under one is a.
Ascendis, Stemler.
So that will be, you know, why Stanway?
Now, here we need to supply for which language you are going to play this Snowbowl stemware.
So let's add the language like Englis.
And let me executed and you will be able to see we created a two object, be this coach, --.
And it has underscored Stemler.
Now, let's apply this stamping process on all this was.
So let me look at every single word.
So let's have for wording words.
Let's bring more.
Plus.
Stambaugh is not this individual word, so we are going to use this P in this court stamp, not stand
and will use this word.
And let me executed it.
And before that, let me put get some differentiating factors.
All right.
So if you execute it, that is the output of.
Porter stammer certain become one.
Then we have a runner.
So then that becomes the nut itself running become run and then become ran.
Drones become Navasky easily become easily.
So this kind of thing.
Now I don't think that this is Zilly and this family or whatever it has reduced to its best form, or
I would say in a good form that is not available might be an extended.
So that may not be valid in dictionary.
Well, let's play something.
Or with Snowball Stemmer.
So understanding algorithms are available.
So instead of FEMA.
Let me make it underscore Stemler and let's see, is there any difference we'll get?
Or not.
All right.
So then become.
Run!
Run, run, run, run.
So first translated related.
Everything remains same.
Even in his.
So we got the same result.
But in fairly it has reduced to not Fadhli but because it is too fat.
So that looks better.
So Snowbowl Stemmer works a little better compared to this quarter.
Stemler.
So that is the whole story behind us.
Coming next is glamorises and now for limitation.
We are going to use this.
Special library and lemonade's isn't really seen.
Next, what you.