A Guide to Feature Engineering in NLP
Overview
- Feature engineering in NLP is understanding the context of the text.
- In this blog, we will look at some of the common feature engineering in NLP.
- We will compare the results of a classification task with and without doing feature engineering
Table of Content
- Introduction
- NLP task overview
- List of features with code
- Implementation
- Results comparison with and without doing feature engineering
- Conclusion
Introduction
“If 80 percent of our work is data preparation, then ensuring data quality is the important work of a machine learning team.” – Andrew Ng
Feature engineering is one of the most important steps in machine learning. It is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Think machine learning algorithm as a learning child the more accurate information you provide the more they will be able to interpret the information well. Focusing first on our data will give us better results