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Mastering spaCy

Solution: Core Operations with spaCy

Let's look at the solution to the previous exercise.

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Solution

The solution to the previous exercise is given below:

Python 3.5
import spacy
nlp = spacy.load("en_core_web_md")
def spacy_tokenizer(text):
    doc = nlp(text)
    tokens = [{"text": token.text, "lemma": token.lemma_,} for token in doc]
    return tokens
def spacy_sentencer(text):
    doc = nlp(text)
    sentences = [sent.text for sent in doc.sents]
    return sentences
def spacy_analyzer(text):
    tokens = spacy_tokenizer(text)
    sentences = spacy_sentencer(text)
    return {"tokens": tokens, "sentences": sentences}
text = "I went for working in Europe. I worked for 3 years in a software company."
result = spacy_analyzer(text)
print("Tokens:", result["tokens"])
print("Sentences:", result["sentences"])

Solution explaination

Let's take a look at this solution:

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