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Natural Language Processing
Добавлен 19 дек 2016
mod12lec62
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1. The translated content of this course is available in regional languages. For details please visit nptel.ac.in/translation
The video course content can be accessed in the form of regional language text transcripts, books which can be accessed under downloads of each course, subtitles in the video and Video Text Track below the video.
Your feedback is highly appreciated. Kindly fill this form forms.gle/XFZhSnHsCLML2LXA6
2. Regional language subtitles available for this course
To watch the subtitles in regional languages:
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Просмотров: 4 304
Видео
mod12lec61
Просмотров 6 тыс.7 лет назад
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mod12lec60
Просмотров 3,6 тыс.7 лет назад
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mod12lec59
Просмотров 3,1 тыс.7 лет назад
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mod12lec58
Просмотров 3,5 тыс.7 лет назад
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mod11lec60
Просмотров 2 тыс.7 лет назад
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mod11lec59
Просмотров 2,1 тыс.7 лет назад
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Tutorial III
Просмотров 1,9 тыс.7 лет назад
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Tutorial II
Просмотров 3,3 тыс.7 лет назад
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mod11lec56
Просмотров 3,5 тыс.7 лет назад
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mod11lec55
Просмотров 3,7 тыс.7 лет назад
To access the translated content: 1. The translated content of this course is available in regional languages. For details please visit nptel.ac.in/translation The video course content can be accessed in the form of regional language text transcripts, books which can be accessed under downloads of each course, subtitles in the video and Video Text Track below the video. Your feedback is highly ...
mod11lec54
Просмотров 3,2 тыс.7 лет назад
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mod11lec53
Просмотров 3,4 тыс.7 лет назад
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mod11lec52
Просмотров 6 тыс.7 лет назад
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mod10lec51
Просмотров 3,1 тыс.7 лет назад
To access the translated content: 1. The translated content of this course is available in regional languages. For details please visit nptel.ac.in/translation The video course content can be accessed in the form of regional language text transcripts, books which can be accessed under downloads of each course, subtitles in the video and Video Text Track below the video. Your feedback is highly ...
Lecture 42 : Topic Models : Introduction
Просмотров 6 тыс.7 лет назад
Lecture 42 : Topic Models : Introduction
Lecture 46:LDA Variants and Applications - II
Просмотров 2,5 тыс.7 лет назад
Lecture 46:LDA Variants and Applications - II
Lecture 45 : LDA Variants and Applications - I
Просмотров 3,6 тыс.7 лет назад
Lecture 45 : LDA Variants and Applications - I
Lecture 44 : Gibbs Sampling for LDA, Applications
Просмотров 6 тыс.7 лет назад
Lecture 44 : Gibbs Sampling for LDA, Applications
Lecture 43 :Latent Dirichlet Allocation : Formulation
Просмотров 7 тыс.7 лет назад
Lecture 43 :Latent Dirichlet Allocation : Formulation
Lecture 41 : Novel Word Sense detection
Просмотров 3,1 тыс.7 лет назад
Lecture 41 : Novel Word Sense detection
Lecture 40 : Word Sense Disambiguation - II
Просмотров 8 тыс.7 лет назад
Lecture 40 : Word Sense Disambiguation - II
Lecture 39 : Word Sense Disambiguation - I
Просмотров 17 тыс.7 лет назад
Lecture 39 : Word Sense Disambiguation - I
Lecture 38: Lexical Semantics - Wordnet
Просмотров 10 тыс.7 лет назад
Lecture 38: Lexical Semantics - Wordnet
Lecture 36 : Word Embeddings - Part II
Просмотров 6 тыс.7 лет назад
Lecture 36 : Word Embeddings - Part II
Explanation was to the point 🤩👍
21:45 i think it should be f2=3f1 as f2 is 150 and f1 is 50 that's why
Its correct. the rank r1, r2 is 50 and 150 respectively. since frequency is inversely proportional to rank, hence we can say roughly f1 = 3f2 (because r2=3f1).
worst teacher i have ever seen yet
padaana chhod do sir apke bas ka nahi hai
कृपया अपने शिक्षक का अपमान न करें
@@vikaschahal1693 kripyaa aap apne kaam se kaam rakhe
@@sharadsisodiya3853 मेरा काम आप जैसे विद्यार्थी का मार्गदर्शन करना है
आप सही कह रहे हैं ये टीचर सबसे ख़राब है
@@vikaschahal1693 HaHaHa What the fuck, fir tune start kyu kiya fight
Wow!!! Enjoyed the lecture thoroughly. Thanks sir!
Well explained, thanks!
I don't wanna disrespect you but i don't understand your way of teaching i saw 18 lacture till now but not getting one of them , in my pov you are worst teacher teacher i have met
Wonderful lecture. Thank you so much sir.
literally made no sense
Great 👍🏻
oh this is so clearly explain. thank you sir
easiest explanation to understand i have ever seen, shukria!
thank u sir for giving some base to visulize morpholgy concept
Thank you so much sir
star wars*
Ek number ka faltu lecture, kuch smj ni pada.
🙏
Info Extraction - Intro
Entity Linking Part 1
Entity Linking Part 2
Doubt here is that , in question it'd given for any word given its a noun or verb values But he takes for a given noun and value as not matching which is simply not true. Can anyone please answer my doubt
The official english translation gives subtitles in tamil?? only auto-translated is available and it doesn't give accurate translation.
Sir assignments tell me sir
Gerry was wrong Now we are concerned about future of AI
What the hell, I tried to learn this thing multiple sources but couldn't understand but the way you explained, I will never forget this
Very Nice
Abe thodi to tez bol bc
This is one of the best n gram videos I've seen. Straight up no cap
Amazing leture Sir Thank You So Much!!
I made her Duck. Interpret it Ppl
I made her a dish called Duck (since D is capitalised, it's a proper noun)
Imagine she is an Australian cricketer, and you are bowling at 130kmph and you bowled a bouncer ball. It is in the height of her head. She might just bend her head, so as to leave the ball . In this case, you can say, "I made her Duck". "Duck" in this context refers to the act of bending head.
I made her suck , autocorrect
I have my exam tomorrow and I could not find anyone who could explain me these topics in such a thorough manner. Thank you sir!
Thank you so much for this video
i will forever be grateful to you.
Pls dont take this course.... You are better off with any other course on NLP .
bro im planning to take the course what's ur opinion, what are the problems u faced, it will be helpful to me if u share
This course is so bad.... The lecturer is not even trying to explain the basics. He is just continuing on his pace. The material is good but the audio and this teacher's teaching skills are zero.
Worst teacher ever seen in my life. He don't even know English properly. His vocabulary is worse. These kind of professors should be fired out immediately of IITs. They are polluting the teaching process....
Those buffaloes blew my mind😂😂 Enjoyed the lecture to the fullest.
😂😂
was these courses created in 2010 ? why arent these updated ????
sir video is not working plzzz upload again
Thankyou you simplified it
merci
uff
12 days ago ... itni planning
One of the Best Explanation on the Topic. Thank you prof. Pawan Goyal
funny thing about the algo is it will get stuck with an infinite loop since he is not updating the variable c when t0 = t*
In electrical engineering, statistical computing and bioinformatics, the Baum-Welch algorithm is a special case of the EM algorithm used to find the unknown parameters of a hidden Markov model (HMM). It makes use of the forward-backward algorithm to compute the statistics for the expectation step.
A Viterbi decoder uses the Viterbi algorithm for decoding a bitstream that has been encoded using a convolutional code or trellis code. There are other algorithms for decoding a convolutionally encoded stream (for example, the Fano algorithm).
A Hidden Markov Model (HMM) is a statistical model which is also used in machine learning. It can be used to describe the evolution of observable events that depend on internal factors, which are not directly observable.
Hidden Markov Model (HMM) is a statistical Markov model in which the system being modeled is assumed to be a Markov process - call it - with unobservable ("hidden") states. As part of the definition, HMM requires that there be an observable process whose outcomes are "influenced" by the outcomes of in a known way.
In linguistics and NLP, corpus (literally Latin for body) refers to a collection of texts.
Naïve Bayes has a naive assumption of conditional independence for every feature, which means that the algorithm expects the features to be independent which not always is the case. Logistic regression is a linear classification method that learns the probability of a sample belonging to a certain class.
Discriminative models draw boundaries in the data space, while generative models try to model how data is placed throughout the space. A generative model focuses on explaining how the data was generated, while a discriminative model focuses on predicting the labels of the data.
Stochastic (Probabilistic) tagging: A stochastic approach includes frequency, probability or statistics. The simplest stochastic approach finds out the most frequently used tag for a specific word in the annotated training data and uses this information to tag that word in the unannotated text.