Word2vec with Gensim – Python

Word2vec with Gensim – Python

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This video explains word2vec concepts and also helps implement it in gensim library of python.

Word2vec extracts features from text and assigns vector notations for each word. The word relations are preserved using this. A famous result of word2vec is King – Man + Woman = Queen .
This concept has lots other applications as well.

Gensim is a library in python which is used to create word2vec models for your corpus.

We Learn CBOW- Continuous bowl of words and Skip Gram models to get an intuition about word2vec.

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By |2019-06-09T19:40:13+00:00June 9th, 2019|Python Video Tutorials|22 Comments

22 Comments

  1. The Semicolon June 9, 2019 at 7:40 pm - Reply

    Thank you for the feedback, Keeping that in mind I have created a very simple but more detailed video about working of word2vec. Link –> https://youtu.be/UqRCEmrv1gQ
    Improved video on Word2Vec with gensim –> https://www.youtube.com/watch?v=Z1VsHYcNXDI

  2. Irshu LX June 9, 2019 at 7:40 pm - Reply

    lol, 8:02; what kind of dataset did u choose?Anyways, Great video for an intro, thanks bud!!

  3. Shwetha Ap June 9, 2019 at 7:40 pm - Reply

    Hello, Please show how to use doc2vec vectors in kmeans

  4. prachi pandya June 9, 2019 at 7:40 pm - Reply

    Hi ur video is very useful.so much simplified implementation thank you..
    Can u make video tutorial for glove model implementation it'll be very helpful..plz

  5. saeb June 9, 2019 at 7:40 pm - Reply

    i am lucky to find this great channel. thank you. would you create video about making skip thought or some sent2vec codes?

  6. Rabia Shah June 9, 2019 at 7:40 pm - Reply

    The data was bad so the result was also not good. Is there any built in gensim training corpus? E.g: Where the closes words to 'hi' would be like 'hello' 'hey' 'hey there'.

    Thanks in advance

  7. venkatarao mannem June 9, 2019 at 7:40 pm - Reply

    AttributeError Traceback (most recent call last)
    <ipython-input-20-0fb11043142e> in <module>

    —-> 1 tok_corpus=[nltk.word_tokenize(sent.decode('utf-8')) for sent in corpus]

    <ipython-input-20-0fb11043142e> in <listcomp>(.0)

    —-> 1 tok_corpus=[nltk.word_tokenize(sent.decode('utf-8')) for sent in corpus]

    AttributeError: 'str' object has no attribute 'decode'

    can anyone help in fixing the error

  8. The Above Mentioned June 9, 2019 at 7:40 pm - Reply

    what is that utf.8 in line number17?

  9. Mandeep Bhalothia June 9, 2019 at 7:40 pm - Reply

    Hi, I have a different question. If we are writing a wrong sentence like I tea drink instead of I drink tea. Can we correct sentence by using word2vec?

  10. Vijay Das June 9, 2019 at 7:40 pm - Reply

    Can we use word2vec for unlabelled text classification? If yes, please advise how.

  11. neuromancer13 June 9, 2019 at 7:40 pm - Reply

    The vector arithmetic at #3:00 doesn't fit. The size and direction of your vectors do not help – they are both wrongs for the purpose mentioned.

  12. Derek Chia June 9, 2019 at 7:40 pm - Reply

    I wrote a guide on how to implement Word2Vec using Python and NumPy. If you are interested to learn about the inner workings, have a look here – https://towardsdatascience.com/an-implementation-guide-to-word2vec-using-numpy-and-google-sheets-13445eebd281

  13. Amr Salama June 9, 2019 at 7:40 pm - Reply

    Arabic Word-Embeddings Word2vec https://www.youtube.com/watch?v=EAv2t6DDqB4

  14. PranjalPathak_WORK June 9, 2019 at 7:40 pm - Reply

    Genasim… Hahaha your audio sucks

  15. KUNAL MAHAJAN June 9, 2019 at 7:40 pm - Reply

    if i have 3 features 'job title' 'job description' 'company description' in my dataset and i have to find whether a job is fake or not then what should i use word2vec or doc2vec? And is it possible to use word2vec and doc2vec for multiple features in a dataset?

  16. Akshay Gaurihar June 9, 2019 at 7:40 pm - Reply

    Hello! I recently started learning of this technology, I have got the basic idea behind that, but couldn't understand the implementation part correctly. Could you please make a detailed video of implementation part? I would be grateful to you! 🙂

  17. Ayush9520 June 9, 2019 at 7:40 pm - Reply

    The video was good and helped me understand word embeddings and word2vec

  18. Anurag Gupta June 9, 2019 at 7:40 pm - Reply

    Great video man….Thanks a lot

  19. Deepak Vadithala June 9, 2019 at 7:40 pm - Reply

    Good explanation. Appreciate the effort. Thank you

  20. portgas ace June 9, 2019 at 7:40 pm - Reply

    hey what about sent2vec ?can it be used for searching semantic word similarity ?if u know pls tell me, i really need it for my graduation project

  21. Nick Kartha June 9, 2019 at 7:40 pm - Reply

    you sound exactly like Prof. Vinay Menon.

  22. Anirudh Agarwal June 9, 2019 at 7:40 pm - Reply

    What is the editor used in the video ?

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