Named Entity Recognition or NER is a technique for identifying and classifying named entities in text. These entities are a level above Part of Speech Tagging and Noun Phrase Chunking where instead of identifying grammatical parts; it's identifying and classifying words as their proper entities. The main categories that are recognized are:
PERSON: People, including fictional.
NORP: Nationalities or religious or political groups.
FAC: Buildings, airports, highways, bridges, etc.
ORG: Companies, agencies, institutions, etc.
GPE: Countries, cities, states.
LOC: Non-GPE locations, mountain ranges, bodies of water.
PRODUCT: Objects, vehicles, foods, etc. (Not services.)
EVENT: Named hurricanes, battles, wars, sports events, etc.
WORK_OF_ART: Titles of books, songs, etc.
LAW: Named documents made into laws.
LANGUAGE: Any named language.
DATE: Absolute or relative dates or periods.
TIME: Times smaller than a day.
PERCENT: Percentage, including ”%“.
MONEY: Monetary values, including unit.
QUANTITY: Measurements, as of weight or distance.
ORDINAL: “first”, “second”, etc.
CARDINAL: Numerals that do not fall under another type.
There are many libraries to choose from; my tool of choice these days is SpaCy [^SPACY]. Its powerful API and models are ready to go with a few lines of code, and as we'll see later, we can use it to train our models. To demonstrate the power, let's take a look at it in action.
Read more here: https://www.dataknowsall.com/ner.html