Limitations to Bag-of-Words

  • It loses the sequence information from the dataset. It just relies on the frequency of words appearing.
  • It creates very sparse dataset since many words tend not to appear in a document.
  • It ignores the context.
  • It doesn’t relate the terms and hence loses the relationship among words.
  • Tend to overfit since so many columns are formed with increasing vocabulary.

Bag-Of-Words Model In R

Effectively representing textual data is crucial for training models in Machine Learning. The Bag-of-Words (BOW) model serves this purpose by transforming text into numerical form. This article comprehensively explores the Bag-of-Words model, elucidating its fundamental concepts and utility in text representation for Machine Learning.

Similar Reads

What is Bag-of-Words?

Bag-of-words is useful for representing textual data in a passage when using text for training and modelling in Machine Learning. We represent the text in the form of numbers generally in Machine Learning. BOW allows to extract features from text using numerous ways to convert text into numbers. It provides two main features:...

Text Classification using Bag of Words

We will be using the CSV file of Poems from poetryfoundation.org from kaggle.com....

Bag-Of-Words Model In R

...

Limitations to Bag-of-Words

...

Conclusion

...