Feature-engineering plays an essential role in developing NLP applications; it is very important for machine learning, especially in prediction-based models. It is the process of transferring the raw data into features, using domain knowledge, so that machine learning algorithms work. Features give us a more focused view of the raw data. Once the features are identified, feature-selection is done to reduce the dimension of data. When raw data is processed, the patterns or features are detected, but it may not be enough to enhance the training dataset. Engineered features enhance training by providing relevant information that helps in differentiating the patterns in the data. The new feature may not be captured or apparent in original dataset or extracted features. Hence, feature-engineering is an art and requires domain expertise. It is still a human craft, something machines are not yet good at.

Chapter 6, Representing Text with Features, will show how text documents can be presented as traditional features that do not work on text documents.