🎯 Key Takeaways for quick navigation: 00:00 🚀 *Introduction to Stemming in NLP* - Stemming process using the Porter Stemmer and NLTK library. - Importing libraries for stemming and stop words. - Explanation of the need for stop words in text processing. 02:56 🛑 *Stop Words and their Removal* - Identification of common stop words like "the," "and," "of" in the paragraph. - Importance of removing stop words for sentiment analysis. - Application of NLTK's stop words library to filter out irrelevant words. 05:53 🔄 *Stemming Implementation* - Initialization of Porter Stemmer object for stemming. - Iterative process of tokenizing, removing stop words, and stemming for each sentence. - Demonstration of stemming on words like "history" and "people." 08:22 ⚠️ *Issues with Stemming* - Discussion on the problem of stemming creating words with no clear meaning. - Examples of words losing meaningful representation after stemming. - Introduction to an alternative stemming technique, "Lemmatization." Made with HARPA AI
Really love the way you explain these concepts with small practical exercises. I have just started your NLP playlist. I don't think I have seen anyone explain these concepts better than you have. A big 👍 to you Krish for such wonderful tutorials.
00:05 Lemmatization is a technique used in Natural Language Processing 01:25 Lemmatization is similar to stemming but the intermediate representation or the root form has a meaning. 02:54 Importing NLTK library for text pre-processing in NLP 04:21 Performing Lemmatization using WordNetLemmatizer 05:46 Lemmatization is similar to stemming but with a small difference 07:09 The process involves removing stop words and performing lemmatization on the remaining words. 08:42 Lemmatization converts words into meaningful representations, while stemming does not. 10:03 The main difference between lemmatization and stemming is that lemmatization considers the meaning of the word.
Krish here you have not lemmatized the stemmed words but you have lemmatized the original paragraph. You basically just removed the stopwords from paragraph. You should have done lemmatization on stemmed paragraph... But very nice explanation gratefull thankyou
Sir, We r very thankful to u for the way u explains and can u do some more on text preprocessing like ALD, removing special characters, white spaces, named entity's all others stuff related to preprocessing
Simply love the way you explain concepts. Can you please make a video tutorial on building a chatbot using NLP or Deep Learning! I couldn't find one in your playlist.
Thanks alot for this, i now understand NLP better, can you make a tutorial on creating a chat bot with this?, i will try my best to spread the word on this channel, you are indeed a great guru
Thank you very much for such tutorials, it has been really a great start for learning....one QQ: How do we avoid lemmatization of some words (some are key words, which we don't want to lemmatize, in stemming we can use regular expression to avoid/skip, but how can we do in lemmatization).
Thanks Krish for video.Just to add on.Lemmatizer.lemmatize will not work as it is giving error "1 missing position argument". Instead we have to use "nltk.stem.WordNetLemmatizer().lemmatize(word)" to avoid error. Hope this will help as video was posted on 2019 and may be there is update in function
On the thumbnail the length od video is larger but when I play videos its length is small For this video thumbail shows 11:18 but when I play it show 6:23
It depends on your use case. Generally, if you're doing sentiment analysis, you, as a user, don't really need to know what the stem word means. In such cases, we use stemming. It's also a lot faster
For a minute, I was reading the awesome speech by Kalam sir.
🎯 Key Takeaways for quick navigation:
00:00 🚀 *Introduction to Stemming in NLP*
- Stemming process using the Porter Stemmer and NLTK library.
- Importing libraries for stemming and stop words.
- Explanation of the need for stop words in text processing.
02:56 🛑 *Stop Words and their Removal*
- Identification of common stop words like "the," "and," "of" in the paragraph.
- Importance of removing stop words for sentiment analysis.
- Application of NLTK's stop words library to filter out irrelevant words.
05:53 🔄 *Stemming Implementation*
- Initialization of Porter Stemmer object for stemming.
- Iterative process of tokenizing, removing stop words, and stemming for each sentence.
- Demonstration of stemming on words like "history" and "people."
08:22 ⚠️ *Issues with Stemming*
- Discussion on the problem of stemming creating words with no clear meaning.
- Examples of words losing meaningful representation after stemming.
- Introduction to an alternative stemming technique, "Lemmatization."
Made with HARPA AI
Really love the way you explain these concepts with small practical exercises. I have just started your NLP playlist. I don't think I have seen anyone explain these concepts better than you have. A big 👍 to you Krish for such wonderful tutorials.
Truly amanzed by the way you explain these concepts 🤗🤗
00:05 Lemmatization is a technique used in Natural Language Processing
01:25 Lemmatization is similar to stemming but the intermediate representation or the root form has a meaning.
02:54 Importing NLTK library for text pre-processing in NLP
04:21 Performing Lemmatization using WordNetLemmatizer
05:46 Lemmatization is similar to stemming but with a small difference
07:09 The process involves removing stop words and performing lemmatization on the remaining words.
08:42 Lemmatization converts words into meaningful representations, while stemming does not.
10:03 The main difference between lemmatization and stemming is that lemmatization considers the meaning of the word.
Awesome tutorials.. keep up Your good work .. 😍
Thanks.
You are doing a Gr8 job Krish
Really super explaination...hat off you
Krish here you have not lemmatized the stemmed words but you have lemmatized the original paragraph.
You basically just removed the stopwords from paragraph. You should have done lemmatization on stemmed paragraph... But very nice explanation gratefull thankyou
6:05 Index - 5, why 'We grabbed land' wasn't converted to 'We grab land'? The lemmatizer is supposed to lemmatize the words in their root form right?
Sir,
We r very thankful to u for the way u explains and can u do some more on text preprocessing like ALD, removing special characters, white spaces, named entity's all others stuff related to preprocessing
Awesome video.... thank you Krish Naik
Simply love the way you explain concepts. Can you please make a video tutorial on building a chatbot using NLP or Deep Learning! I couldn't find one in your playlist.
Thanks alot for this, i now understand NLP better, can you make a tutorial on creating a chat bot with this?, i will try my best to spread the word on this channel, you are indeed a great guru
Really helpful and well explained!
Superb video Krish.Thanks
The tutorials are really good and well explained.
Could you by any chance add the Stanford article link in the description. Thank you.
Sure here is the article
nlp.stanford.edu/IR-book/html/htmledition/stemming-and-lemmatization-1.html
Please share with all your friends who all are looking for such kind of tutorials
Thanks Krish
Thank you very much for such tutorials, it has been really a great start for learning....one QQ: How do we avoid lemmatization of some words (some are key words, which we don't want to lemmatize, in stemming we can use regular expression to avoid/skip, but how can we do in lemmatization).
Nice tutorial brother
Thankyou sir❤️🔥
Good channel. It is very helpful. I've subscribed your channel.
Does Lemmatization actually converts the meaningful word? Or keep them as it is? For e.g. history is as history only after Lemmatization
Sir, no change is observed before and after lemmatization. What to do? I have tried with smaller passages as well but no change.
Sir thank you so much these videos very helpful for me sir i want NER videos
Niceky explained
Krish one question, it wouldn't be recommended to lowercase the words, so stopwords like "I" , "In", "It", "The" get removed too ?
Thanks Krish for video.Just to add on.Lemmatizer.lemmatize will not work as it is giving error "1 missing position argument".
Instead we have to use "nltk.stem.WordNetLemmatizer().lemmatize(word)" to avoid error.
Hope this will help as video was posted on 2019 and may be there is update in function
On the thumbnail the length od video is larger but when I play videos its length is small
For this video thumbail shows 11:18 but when I play it show 6:23
What is the 'u' after invade? thanks
bro why we need to do stemming ,can directly do the lemmatization right?
It depends on your use case. Generally, if you're doing sentiment analysis, you, as a user, don't really need to know what the stem word means. In such cases, we use stemming. It's also a lot faster
I don't know why my attention always go to how much battery charge is remaining in admin's laptop.. :D
Haha. I see the same when someone sends me screenshots.
thanks
Thank you very much
Nice sir
im not getting space between words why is that can someone help?
sir please make video on chatbot application
lemmatization is not working for me
what is the error
I have some different opinion