Semantic annotation captures the tagging of certain documents and concepts that are relevant to the data. This involves including metadata into documents that will enhance the content with deceptive words and concepts to offer meaning and depth to the text. The benefit of semantic annotation is that it enhances product listings and at the same time, customers can locate the products or services they are looking for. Consequently, it helps in turning the browsers into potential buyers. When you tag the numerous components under product titles and look for queries, this type of annotation solution trains the algorithm to identify those independent components and maximise the relevance of the search on the whole.
Named Entity Annotation
NER (named entity recognition) is used to spot specific entities under text to identify crucial data for larger datasets. Brand names, formal names and places are some of the instances detected by this type of annotation. NER annotation needs a huge amount of training data that is annotated manually. The annotators use the NER capabilities for many use cases by assisting e-commerce customers to recognize and tag key descriptors or helping social media sites to tag entities like places, people, titles and companies to help target the ad content better.