Bag-of-Words
Explore the Bag-of-Words technique to convert text into numerical vectors by counting word frequency. Understand its advantages, limitations, and practical implementation using Python libraries. This lesson helps you build solid text representations for various NLP tasks without relying on word order or context.
We'll cover the following...
Introduction
The bag-of-words (BoW) is an essential technique to represent text data in a numerical format that machine learning algorithms can understand. We normally use this technique when we’ve cleaned the text data and need to use it for machine-learning model training. It allows us to treat text data as an unordered collection of words and disregard grammar, word order, and context. As a result, we find its application in scenarios where the context or sequence of words is less important than the frequency of individual words.
Calculating BoW
Let’s consider a simple BoW calculation for ...