A Textually Evoked Entity (TEE) refers to a specific, identifiable object, person, place, or concept that is mentioned within a piece of text. These entities are not abstract ideas but concrete references that can, in principle, be linked to the real world. Identifying TEES is a fundamental task in Natural Language Processing (NLP).
The core idea is to move beyond generic word mentions to pinpoint specific instances. For example, in the sentence “Apple announced its new iPhone,” Apple refers to the specific company, and iPhone refers to a particular product line.
While related to Named Entity Recognition (NER), TEES can be broader or more specific. NER typically focuses on predefined categories like persons, organizations, and locations. TEES can encompass abstract concepts or specific instances not covered by standard NER tags.
Identifying TEES involves several steps:
TEES are vital for:
A common challenge is distinguishing between generic mentions and specific entities. For instance, “I want an iPhone” might refer to the product generally, while “Tim Cook announced the iPhone 15” refers to a specific instance. Ambiguity is also a significant hurdle.
A mention is any word or phrase in the text that could potentially refer to an entity. A TEE is a mention that has been confirmed to refer to a specific, identifiable real-world entity.
Yes, TEES can include abstract concepts if they are specific and identifiable within the context, such as “the Paris Agreement” or “the theory of relativity.”
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