Data Annotation: "labeling" target vs features I understand how one would use a data annotation tool to label targets for a given sentence, for example though, I'm not clear on how placing labels on features can be used to improve model performance.  For example, in this text annotation tool , you can add &quot;labels&quot; to a body of text like person, location, event ...etc .  Given that you must create Word Embeddings to work with the data, and the vector representation is not human-readable, how would you be able to improve model performance by annotating feature variables?
Those labels are not primarily for features, those labels are primarily for targets. Person, location, and event for targets for named-entity recognition (NER).