# pip install -U spacy
# python -m spacy download en_core_web_sm
import spacy

# Load English tokenizer, tagger, parser and NER
nlp = spacy.load("en_core_web_sm")

# Process whole documents
text = ("Apprenticeship Assigned."
"Apprenticeship S101 is assigned to Peter Johnson."
"Apprenticeship S209 is assigned to Mary Blight."
"Apprenticeship Category."
"Apprenticeship S101 falls in the ICT."
"Apprenticeship City."
"Apprenticeship S101 takes place in New York."
"Apprenticeship Description."
"Apprenticeship S101 concerns the development of a magazine infomration system."
"Apprenticeship Duration."
"Apprenticeship S101 will take 4 months."
)
doc = nlp(text)

# Analyze syntax
print("Noun phrases:", [chunk.text for chunk in doc.noun_chunks])
print("Verbs:", [token.lemma_ for token in doc if token.pos_ == "VERB"])

# Find named entities, phrases and concepts
for entity in doc.ents:
    print(entity.text, entity.label_)