Spacy Part 2
Learn more details about Spacy in this lesson.
We'll cover the following
Named Entity Recognition
If we check Wikipedia we get to know the following definition of Named Entity Recognition (NER).
Named Entity Recognition (NER), also known as entity identification, entity chunking, and entity extraction is a subtask of information extraction that seeks to locate and classify a named entity mentioned in unstructured text into pre-defined categories. These categories could include things like person names, organizations, locations, medical codes, time expressions, quantities, monetary values, percentages, etc.
Spacy provides the following named entities.
Type | Description |
---|---|
PERSON | People, including fictional. |
NORP | Nationalities or religious and political groups |
FAC | Buildings, airports, highways, bridges, etc. |
ORG | Companies, agencies, institutions, etc. |
GPE | Countries, cities, states. |
LOC | Non-GPE locations, mountain ranges, and 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. |
Coding exercise
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