Named Entity Recognition (NER) and named entity disambiguation (NED) are two interrelated tasks in natural language processing (NLP) that deal with identifying and resolving named entities in text.

  Named Entity Recognition refers to the process of identifying and classifying named entities in text into predefined categories such as person names, organization names, location names, and so on. NER systems typically use machine learning techniques, such as statistical models or deep learning algorithms, to train on annotated data and learn to recognize entities in new text.

  On the other hand, named entity disambiguation is the task of resolving the ambiguity present in certain named entities. Ambiguity arises when a named entity, such as "Apple," can refer to different entities like the technology company or the fruit. NED aims to determine the correct entity reference based on the context in which the term appears.

  NER and NED are closely related because NED is often performed as a subsequent step to NER. After identifying the named entities in text, the next step is to disambiguate these entities to determine their intended meaning or reference. NER provides the initial set of named entities, while NED further refines this set by disambiguating them.

  NED can be achieved using various approaches. One common approach is to utilize knowledge bases, such as Wikipedia or Freebase, to link the identified entities to specific entries in these sources. By comparing the characteristics and contextual information of the named entity with the information in the knowledge base, NED systems can determine the correct entity reference.

  Overall, NER and NED work together to extract and disambiguate named entities in text. NER identifies the entities, while NED resolves any ambiguity in their references, enabling downstream applications like information retrieval, question answering, or semantic analysis to make more accurate interpretations and decisions based on the identified entities.