All right, everyone.
So next one is freeze is melting.
So in our last video, we have seen Rule-based making now freespace.
Amazing.
So let me look at this one.
Now, Fresia means whenever you are trying to achieve or some work, which is a combination, let's
say something like the phrase you are searching for, but space Obama or let's say pendulous place Marka,
harder, Washington, D.C., so forth.
Let me help to create one face.
Metcher object.
Let me execute it.
And there is a part of again speci that matcher module only necmi defined.
All this key phrases.
Now you just did not supply all those phrases.
Basically we know how to apply to the other NHP model.
And for that, we'll use this list comprehensive.
And for each PAX inside this phrase list, we are just applying to the NLB and created document Objects
Holtorf.
So this particular thing, phase baktun will be a list of all those document objects.
Let me run it.
Let's display this phrase pattern, so that will get it back that and God define if you find the type
of the very first phrase pattern, it will be kind of duck.
Next this.
We need to have this phrase pattern over matched object.
So you can see the matches we have created.
But now this matches like a phrase matched object.
So let me add it.
And you can see I've kept here stop.
So STK, we need to keep it.
Next, this let's define our document and apply it to the MLP model.
So we will create a document object and let's find matches.
So for matches V, how to apply this document to the matched object.
Let me run it.
And it will find the matches for us.
Now, as of now, it is just given a simple index number only.
But let's see the detailed description of those.
Barack Obama, Angela Merkel and Washington, D.C..
So you can see the very first thing is getting.
Just the string representation of our match up.
So we hope, given the string representation of this particular patterns will be terminology list.
So everyone, it will be a terminology list so you can even define multiple also.
So let's see if you find something else like.
Or if you add something else, like another terminology.
So you'll be able to find it in this particular phrases like match due to these kinds of phrases.
We help define and just like earlier, we have seen in a rule-based matting it is trying to extract
where in a particular place in our document object.
We got this.
Match for.
So from two to four, we got this.
Angela Merkel day in from 79.
It will be Barack Obama and from 19 to 22, it's of Washington, D.C..
So that is how easily you can do the phrase mismatching inside this space.
NetSol, about this video.
See you in the next video, we'll see some more stuff related to natural language processing.