What is gullibility in Natural language processing?

Now on the one thing that I talked about with the doc, so that was the dominant object the object in the club symptoms So they could This stuff will be useful in that this could be useful You want to do fax from a news article Ah fact is nothing about the subject Weldon objects factors basically so So I also want things like some I think labeling, so that is a thing called gullibility Wanted to go who did walk in that sense.

It’s a useful effect extraction from a large purpose Somebody gives you know you bring in a new country and you are saying to this guy that you want to sell something in that country you want to do so say you don’t know anything about that country What could you do So if you have access to last one-year newspapers in the country you can run the studio automatically pick it all facts and then think it’s doing that white and so on.

That could be really useful Britney understands a lot of set of documents or someone alive That’s uh But this is just more detail on the first off dependency structure says dependency that is finally in nature So they have the link between words So they just between Russia and they describe the relationship between the between So essentially it has ahead in the head off the front of the door on uh depending dependent But also useful Passive voice was actually centers of subject pregnancy fashion sense of deposit dependence the best on the dependency You can figure out how many said that that was how many possibles so that we discussed already So that is all.

That is pretty much the end of literacy What we discussed Hopefully not somebody last you know you can actually say is I was somewhat about beating So they talked about what is helpful in what’s our fast fall into this big So we talked specifically evolved the basic models that are required to build any big NLP system so open innovation normal relations stemming sometimes segmentation of unifying with after getting out sentences flavor identification What is this an immigration on a little block passage Yeah it was a large top.

It’s the last media So essentially like I mean uh many books on an empty essentially the next things that we could state of discussing after this would be like popular sentiment Analysts are perfect in the detainee population I think they hold on to some of the tradition And so so for the story of stuff with that makes its political analytics So if you basically want toe you know, so analytics is envious Tedious as a disgusting I think I can go back to the first state if you want the analyst and signal for me What percent off the customer business making your They called it was going to get that meets and then figure out something timeless is on top of them That’s basically a stick example.

Now I think this necklace start making boosted just his applications that use something natural language, for example, It’s looking died of your application is probably no noise looking here but profited comics analysis So let’s say you have uh basically allow discussions between the power and the pilot and the ground staff are all recorded on There are using speech recognition systems Now I have up in this bag If anything happens to the plate you can always go back to the recording and then do some safety Meantime listen sticking on what is going on A cross accident on he said Anything human that profits within it Tom Analysis can be done lived on these things.

If there are based on these charts blocks you can go a lot of analysis in that sense in various applications Russian video analytics No they thought the boat sentiment the festival interactions user profiling in e-commerce and retain Looking at what kind of is you’re putting in more things in the first Nice for you if you can go natural language processing and get out of that window Li Bad’s more off on my photo Robin wood off various such application What begins to start if it’s okay for the first time Big What if their spelling mistakes right So are fattening is usually very good at correcting spelling mistakes Why Because they have learned from previous uses no small message and then you have some spelling mistakes in it.

I manifest many mistakes today There are switched off my experience direction because uh you can install them on your machine on they make you have simple things like two characters on the typewriter are People are frequently have changed, so those are perfect because spelling mistakes So the first Finnish people make spelling mistakes and then the ones we saw importer are people make spelling mistakes when this very difficult one like Mississippi on the movie I and so on And then they have that Nothing’s in family See in the model no makings off them in your now for the second bush it is like love it with that.

You should start with this kind of thing are being packages good So just play for Houston package Read it so I mean everyone bizarre jobless You connect Big Nick Uh just look at what is regulated The I d Any eat meat, Yes Investigated gives you help fight for our party and package And then there are many examples which are typical in that fight So the examples can help yourself are going Booth thinks I mean nice examples discussed What would you do with the impact that’s that one should wait on that he could run it on as many Azumi document as you like what So the Democrats you everything that’s off of a second And I was since this offer boots past please which we did not in the in there which we’ve not seen that huh But I I did not have the bride five but this is going to Stanford.

When I say usually documented minutes Ah text document It could media also but you usually can article picks before doing it X tactless is I mean usually expired TXT file Yeah Any of that quick questions Yeah Sorry I was found not more attentive to this part of the class but I don’t know I mean that that has something to do with the male Probably like really a classroom But it so so I mean it depends on what application you’re talking about So there are various reasons why people switch context and various applications For an example of inviting a new story I might talk about something and then six to another topic And then we’ll talk about the first topic again and saw so that things got topic margins which it did.

Leave a Comment