Named Entity Concept
Recognition and disambiguation of named entities have their origins in computational linguistics.
These disciplines, which were later accepted as sub-tasks of information extraction, quickly attracted the attention of different scientific fields such as biology and biomedicine or application areas such as semantic web and information sciences.
Initially, the proposed concept of named entities encompasses the concepts of time, currency, and percentage, as well as the names of people, organizations, and geographic locations.
The named entity policy is defined similarly. Expressions containing the names of people, organizations, places, times, and quantities.
However, this double definition is rather vague. It has been replaced by the use of NER by different fields. That’s why we now include products, diseases, or events in the list of named entities.
There is this lack of consensus in the integration of new categories of named entities. It is specified that this limits it to an entity specified by a strict identifier as defined.
A solid signifier specifies the same object in all possible worlds in which that object exists, and never anything else.
There is an obvious lack of consensus on the definition of the named entity because it often sticks to the domain in question.
The unique complexity of each application area makes it difficult to establish a solid definition. It makes it impossible to create a universal extraction system. To customize the definition of an entity named by field, it is necessary to propose an approach based on a list of defined criteria.
This list takes into account the limitation, scope, and level of detail of an asset. Indeed, we see some NER tools use production tokens.
It is noticed that others do not take them into account. It turns out that the concept of a named entity is never absolute, but dependent on both the operand integrity and the implementation.
In the French-speaking world, efforts have been made to classify named entities.
For clarity, we have chosen to use the term entity to refer to any entity, whether or not it meets its definition.
We also use the acronym NER to name both literal recognition of named entities (Named Entity Recognition) and terminological extraction (Term Extraction).
NER and the Semantic Web
The NER technique is heavily dependent on the knowledge bases used to train the named entity extraction algorithm.
By mobilizing resources, recent projects have made it possible to match entities and facts using these ontologies.
Efforts have been made to go further than the simple identification of a named entity and its type.
It is mainly engaged in disambiguation through a URI (Uniform Resource Identifier, unique resource identifier).
Lexical semantic disambiguation is one of the biggest challenges in natural language processing. Indeed, natural language, unlike formal or programming languages, is fundamentally ambiguous.
The principle of Vocabulary Disambiguation (lexical semantic disambiguation) takes its full meaning and remains a problem to be solved.
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Also, a person or organization may have several names or titles. WSD methods take action via text associated with extracted assets. It tries to contextualize vague terms to discover their exact meanings.
Therefore, a named entity extraction service aims to analyze input data to detect named entities. It then aims to give them some sort of assignment based on their trust score.
