Natural Language Processing | CKY Algorithm & Parsing | CFG to CNF | Probabilistic CKY | Numerical

[@GULLAHARSHAVARDHAN]

This man earned a lot of respect literally 💌

[@aizazkhan5439]

I really wanna thank you Mr. Binod. This was made so easy to understand to your genius.

[@nikhilbonala4063]

Thank you so much sir. I’m trying to learn this concept from different sources but yours was clear.
Most underrated UA-cam channel it seems!!

[@binodsuman]

Thank you Nikhil for your kind words. Despite my UA-cam Channel being underrated, the comments on my recent video have made me proud of my teaching abilities. Your motivation and encouragement have inspired me to create more videos. Let's keep learning together!!

[@kandamudipavani3216]

sir ur way of explanation is simply superb

[@srinivaskedarisetty3437]

Thanks Binod, its really simple to understand. When i go thru your video - you make concept easy to understand & quickly grasp it. Thanks once again

[@priyanshkakani]

sir brilliant explaination thanks for teaching respect your efforts

[@deepikaselvamurugan6405]

Very useful video! Clear explanation on the concept and easily understandable numerical.

[@kqubes2511]

Thanks a lot sir, for explaining it clearly you really helped me understand this concept better than any other lecturers present

[@surajvijay1937]

tmrw is my exam. watching this video boosted my confidence. easy to understand

[@sasikanthpvs]

Thank you Binod for your videos. it helped for exams!

[@binodsuman]

Glad to know this NLP Natural Language Processing tutorial video helped you. Thanks for your nice words !!

[@unique_bhanu]

Wonderful explanation ❤sir.Thank you

[@veskibateman2070]

Fantastic explanation. Thank you Binod!

[@caesaralpha1280]

this is a super helpful explanation. Thanks for making this video..

[@nihalyadalam8933]

Thank you for explaining this clearly

[@binodsuman]

Happy to hear Nihal, that this Amazon NLP Natural Language Processing CKY videos Tutorial series helped you. Keep Learning!! @binodsumanacademy

[@RGB_321]

Thank you sir you cleared me this topic

[@joshuawilkins3656]

I could be wrong but I calculated a different final probability. In chart[0,5] I get 2.304x10^-8

[@joshuawilkins3656]

Thanks for the explanation though! Very much enjoyed

[@dhruvgupta1565]

Thank you ! This was an amazing explanation

[@binodsuman]

You're very welcome! Goos to know, this CKY video helped you.

[@thibaultlequere2052]

Hello, so if there is more than one parse given by the probabilistic CKY you choose the more probable?

[@zhongzhong5851]

Thanks for your video, very helpful and understandable

[@binodsuman]

Glad this NLP video was helpful for you! Keep Learning !!

[@SophieMcKeown-Dillon]

explained so well thanks so much

[@NidhiSingh-bw7sb]

Thank you Sir ! You make subject interesting.

[@binodsuman]

Glad to hear Nidhi that this Natural Language Processing NLP Tutorial series helped you. Keep Learning and thank you for your nice words !!

[@lamnguyentrong275]

thank you sir, great explanation

[@NidhiSingh-bw7sb]

Big thanks 🙏.
Sir ! Please explain Chu- Liu-Edmonds algorithm

[@nafassaadat8326]

Thank you Soooooo Much it was really helpful

[@binodsuman]

Good to know this NLP CKY Algorithm UA-cam Video helped you. Thank you for nice words. Keep Learning !!

[@mohammadzahrawi5713]

What a lovely teacher, keep it up 🙃🌹

[@tahmidimran1840]

clear and concise!

[@susmitagoswami9928]

Thank You, It is a nice video.

[@miteshjoshi9384]

good explaination, it's also help to implement through programs

[@palomoshoeshoe7228]

Thanks Sir, this has been very helpful, i really appreciate

[@hussainwali7522]

Thank you sir

[@SanikaRane-z6c]

Why did NP and v didn't connect in 0,3 box ??

[@a_08_apoorvakc25]

This is really helpful, thank you ! :))

[@binodsuman]

Glad this Natural Language Processing tutorial was useful for you, Keep Learning !!

[@oindrilamondal6501]

Thank You, Sir.

[@MohamedOsama-kl6ex]

❤

[@gopivardhangunta8553]

Thanks Soo much👌

[@Pretence404]

awesome

[@SiriChandana-f9r]

Apply probabilistic CKY Parsing of PCFG to the sentence “John eats pie with ice-cream” and update the table.
Production rules Probabilities
S → NP VP 1.0
NP → N 0.4
NP → NP PP 0.6
VP → V NP 0.7
VP → VP PP 0.3
PP → P NP 1.0
N → "John" 0.2
N → "pie" 0.3
N → "ice-cream" 0.5
V → "eats" 1.0
P → "with" 1.0
can you please explain for this?

[@deepikasingh3122]

will the set of rules be given in the question paper or do we have to do that on our own

[@noodumaga]

Hi, pls add more videos on the continuation of CKY, chart parsing, PCFG, dependency parsing, transition parsing. Thanks for your lectures but need more videos.

[@thib610]

how precision can be achive with probabilistic cky algorithm?

[@md.shafaatjamilrokon8587]

Please complete the playlist sir

[@mulyevishvesha1283]

Plz make video how you calculate so fast

[@debashishtiu2919]

parse tree ??

[@AnitaDevi-l2i2e]

sir aapne isme error kiya hai 0.3*0.4+0.02 =??

[@eliyachristophernandi6306]

💯

[@keshavmaheshwari521]

sir, which book you are referring to?

[@nehabalhara2114]

Nice sir

[@soumyasarkar4100]

by any chance are you from Assam ?

[@sameeryusuf4555]

What is probability is not given

[@aurkom]

Next video when?

[@rajgothi2633]

The IIT professor failed to teach the CYK algorithm as effectively as you did.

[@Cooldude5786]

Binod Suman I think calculation at the end is wrong

[@parassachdeva3816]

Binod

[@vijhey3850]

Binod

[@raht9578]

Binod

[@binodsuman]

ha ha Binod