Named Entity Recognition (NER) and Entity Linking (EL) are two important tasks in natural language processing and information retrieval. Although they are related, they differ in their objectives and approaches.

  Named Entity Recognition aims to identify and classify named entities in text, such as people, organizations, locations, dates, and other specific types. The goal is to extract and classify these entities accurately. NER typically involves using machine learning models, such as sequence tagging or classification algorithms, trained on labeled data to identify and classify entities.

  Entity Linking, on the other hand, goes beyond NER by linking the identified entities to a knowledge base, usually a semantic network or structured database. It aims to determine the specific entity (e.g., person, organization) that a mention in the text refers to. Entity Linking involves disambiguating entity mentions by mapping them to the corresponding entities in a knowledge base. This process is often referred to as entity disambiguation or entity resolution.

  The main difference is that NER focuses on recognizing and categorizing named entities within a given text, while EL aims to link these recognized entities to a knowledge base. NER is a fundamental step for EL as it helps to identify the relevant entities in the text, which are then linked to the corresponding entities in the knowledge base.

  In practice, NER is often used as a precursor to EL. Once the entities are recognized in the text using NER, EL is performed to link them to specific entities in a knowledge base, providing additional information and context.

  To summarize, Named Entity Recognition is the process of identifying and classifying named entities in text, while Entity Linking goes a step further by linking these identified entities to a knowledge base.