Labelling and annotation are both used in machine learning and AI. They are used to form data sets for NLP-based (natural language processing) language or voice recognition technology. The texts are mostly labelled or annotated to make the useful words and keywords understandable to machines and aid them to respond correspondingly.
When it comes to text labelling, it is performed for sentiment analysis and many such purposes primarily in AI and machine learning. When you compare annotation with labelling, labelling is more complex and can be used for NLP algorithms. Annotations are used for perception models that are visual-based.
Text annotation aids in visualising the significant words or texts with the help of computer vision. As far as text labelling is concerned, the texts are marked and metadata is included in every word as it helps in language processing integration. But both need expertise and to be performed with precision to ensure that the significant words can be utilised to define the correct meaning and also develop an NLP or AI based model. With the help of special software or tool, annotation and labelling of texts can be done with the greatest level of precision.