Named-entity recognition

What are Named Entities?

Named entities are sets of elements that are important to understanding text. Some common entities come from parts of speech (like nouns, verbs, adjectives, etc). Nouns in particular are essential in understanding the subtle details in a  sentence. Thus, we look more into nouns than other parts of speech when working with named entity recognition, which will be explained below.

Named Entity Recognition Explained

In Natural language processing, Named Entity Recognition (NER) is a process where a sentence or a chunk of text is parsed through to find entities that can be put under categories like names, organizations, locations, quantities, monetary values, percentages, etc. Traditional NER algorithms included only names, places, and organizations. However, they can now be dynamically trained to extract more than just the previously mentioned entities. NER is a simple but effective approach to reduce searching a state space by directing the algorithm to weigh the sentences more if a chunk of entities are found.

Example of under the hood NER