An entity name can be anything referred to with a proper noun that constitutes a specific type of lexical unit related to certain domains that have a proper name, such as human, geographic locations, organizations, and more. It also includes numerical expressions and names of particular classes like drugs, diseases, arts, works, etc.
Traditionally, the term entity name is used to refer to a person, location, or organization. Later, the term encompassed date, time, and quantity. The following is a broad classification of entity names:
Names: The first and foremost classification of a named entity is a name, which can be anything with a proper name, such as persons, locations, and organizations.
Quantities: Entity names include the expression of quantities that may be measurement units or percentages.
Dates and duration: Dates and times also come under the category of named entities.
How are entity names used?
In business analytics, an entity name is a proper name that differentiates an item from a set of items that have similar attributes. Examples of entity names include full names, ages, addresses, locations, and so on.
Business entity names play a major role when starting a business, as the law should recognize a business as a separate entity to sign contracts and acquire rights and privileges. Generally, there are four broad categories of business entities:
- Sole proprietorships
- Limited liability companies
Two methods to locate and identify entity names
Identifying named entities from unstructured data is a simple process through natural language processing and machine learning systems. Significantly, there are two methods to identify entity names: named entity recognition and named entity disambiguation.
Named Entity Recognition: Named entity recognition (NER) is an entity identification method used to identify entity names in a text by using natural language processing (NLP). NER automatically identifies and categorizes them based on a predefined structure. With NER, the user can set the different entity categories such as name, time, location, etc., and tag some samples with each category to train the NER model to identify them. NER also allows the user to understand what the text is about and extract significant information without having to read a complete text.
Named Entity Disambiguation: Named entity disambiguation (NED) or entity linking (EL) is the process of linking or mapping entity names. NED uses parametric learning models to identify named entities from a text with the unique graph-based formal entity names in a knowledge base like Wikipedia, WordNet, and so on. However, the parametric learning technique does not scale the data well when the number of formal entity names exceeds thousands. Therefore, NED also uses a deep learning structure to evaluate entity names with Wikipedia sources.