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EPISODE · Oct 9, 2020 · 4 MIN

Bag of Words in Natural Language Processing

from Code Logic · host Sarvesh Bhatnagar

In this podcast episode we talk about bag of words model in natural language processing. Bag of Words model is simply a feature extraction method used in NLP. We mainly discuss about why bag of words model is required and what it is. In summary BOW is simply a set of tuples with words along with their frequency pairs. To learn more about BOW : visit this Gensim Introduction : visit this Also, to support me do visit www.stacklearn.org

What this episode covers

In this podcast episode we talk about bag of words model in natural language processing. Bag of Words model is simply a feature extraction method used in NLP. We mainly discuss about why bag of words model is required and what it is. In summary BOW is simply a set of tuples with words along with their frequency pairs. To learn more about BOW : visit this Gensim Introduction : visit this Also, to support me do visit www.stacklearn.org

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Bag of Words in Natural Language Processing

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Frequently Asked Questions

How long is this episode of Code Logic?

This episode is 4 minutes long.

When was this Code Logic episode published?

This episode was published on October 9, 2020.

What is this episode about?

In this podcast episode we talk about bag of words model in natural language processing. Bag of Words model is simply a feature extraction method used in NLP. We mainly discuss about why bag of words model is required and what it is. In summary BOW...

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