Understanding feature engineering
Before jumping into the feature generation techniques, we need to understand feature engineering and its purpose.
What is feature engineering?
Feature engineering is the process of generating or deriving features (attributes or an individual measurable property of a phenomenon) from raw data or corpus that will help us develop NLP applications or solve NLP-related problems.
A feature can be defined as a piece of information or measurable property that is useful when building NLP applications or predicting the output of NLP applications.
We will use ML techniques to process the natural language and develop models that will give us the final output. This model is called the machine learning model (ML model). We will feed features for machine learning algorithms as input and to generate the machine learning model. After this, we will use the generated machine learning model to produce an appropriate output for an NLP application.
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