Consider the use of entity identification and categorization.
a. What problems are they trying to solve – what are they trying to do.
b. How do they affect the list of processing tokens in an item?
c. How do they affect the Document Vector for the item?
a. Entity identification and categorization are used to solve the problem of organizing and understanding information within texts or documents. By identifying and categorizing entities, such as names, places, organizations, or date expressions, the goal is to extract meaningful information and gain insights into the content of the text. This can help in various tasks like information retrieval, sentiment analysis, document summarization, and more.
b. Entity identification and categorization can impact the list of processing tokens in an item by adding or replacing certain tokens with corresponding entity tags. Instead of considering individual words as tokens, entities are recognized as a whole and assigned a specific tag. For example, instead of "John Doe" being treated as two separate tokens, it will be identified as a single entity token with a label indicating that it represents a person's name.
c. Entity identification and categorization affect the Document Vector for the item by adding additional features or dimensions related to the recognized entities. The Document Vector is a representation of the document's content in a numerical form. By including entity information in the Document Vector, it becomes enriched with entity-based features, allowing for more sophisticated analysis and comparison of documents based on their entity composition. This can be useful in tasks like document clustering, topic modeling, or recommendation systems that rely on understanding the entities present in the document.