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Mastering spaCy

Solution: Linguistic Features

Explore how to apply spaCy for key linguistic features such as part-of-speech tagging, dependency parsing, and named entity recognition. Understand how tokens are processed and labeled, enabling you to analyze grammatical structures and entities within text using spaCy.

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Solution

In the code snippet below, we have given the solution to the previous problem.

Python 3.5
import spacy
nlp = spacy.load("en_core_web_md")
text = "Apple is looking at buying U.K. startup for $1 billion"
doc = nlp(text)
for token in doc:
    print(f"{token.text} {token.pos_} {token.dep_}")
for ent in doc.ents:
    print("{ent.text} {ent.label_}")

Solution explanation

  • Lines 1 and 2: We import the spacy library and load an English model using spacy.load. ...