Difference between revisions of "Continuous Bag-of-Words (CBoW)"
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| − | [ | + | [https://www.youtube.com/results?search_query=Continuous+Bag+Words+cbow+nlp+natural+language YouTube search...] |
| − | [ | + | [https://www.google.com/search?q=Continuous+Bag+Words+cbow+nlp+natural+language ...Google search] |
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| − | The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words). Considering a simple sentence, “the quick brown fox jumps over the lazy dog”, this can be pairs of (context_window, target_word) where if we consider a context window of size 2, we have examples like ([quick, fox], brown), ([the, brown], quick), ([the, dog], lazy) and so on. Thus the model tries to predict the target_word based on the context_window words. [ | + | The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words). Considering a simple sentence, “the quick brown fox jumps over the lazy dog”, this can be pairs of (context_window, target_word) where if we consider a context window of size 2, we have examples like ([quick, fox], brown), ([the, brown], quick), ([the, dog], lazy) and so on. Thus the model tries to predict the target_word based on the context_window words. [https://towardsdatascience.com/understanding-feature-engineering-part-4-deep-learning-methods-for-text-data-96c44370bbfa A hands-on intuitive approach to Deep Learning Methods for Text Data — Word2Vec, GloVe and FastText | Dipanjan Sarkar - Towards Data Science] |
| − | + | https://miro.medium.com/max/542/1*d66FyqIMWtDCtOuJ_GcqAg.png | |
<youtube>yBmtXtVya9A</youtube> | <youtube>yBmtXtVya9A</youtube> | ||
Revision as of 06:21, 28 March 2023
YouTube search... ...Google search
The CBOW model architecture tries to predict the current target word (the center word) based on the source context words (surrounding words). Considering a simple sentence, “the quick brown fox jumps over the lazy dog”, this can be pairs of (context_window, target_word) where if we consider a context window of size 2, we have examples like ([quick, fox], brown), ([the, brown], quick), ([the, dog], lazy) and so on. Thus the model tries to predict the target_word based on the context_window words. A hands-on intuitive approach to Deep Learning Methods for Text Data — Word2Vec, GloVe and FastText | Dipanjan Sarkar - Towards Data Science
