texthero.nlp.named_entities¶

named_entities(s, package='spacy')¶

Return named-entities.

Return a Pandas Series where each rows contains a list of tuples containing information regarding the given named entities.

Tuple: (entity’name, entity’label, starting character, ending character)

Under the hood, named_entities make use of Spacy name entity recognition.

List of labels:

• 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.

Examples

>>> import texthero as hero
>>> import pandas as pd
>>> s = pd.Series("Yesterday I was in NY with Bill de Blasio")
>>> hero.named_entities(s)[0]
[('Yesterday', 'DATE', 0, 9), ('NY', 'GPE', 19, 21), ('Bill de Blasio', 'PERSON', 27, 41)]