Spacy Library in Python: Learn Now

Spacy is a popular open-source library used for Natural Language Processing (NLP) in Python. It was developed by Matthew Honnibal and Ines Montani and is known for its speed and accuracy. In this article, we will explore Spacy and how it can be used for NLP tasks.

Installing and Importing Spacy

Before we can start using Spacy, we first need to install and import it into Python. Installing Spacy is easy and can be done using pip. Open your terminal or command prompt and type the following command:

pip install spacy

Once Spacy is installed, we can import it into Python using the following command:

import spacy

Basic NLP Tasks with Spacy

Spacy can perform several basic NLP tasks such as tokenization, Part-of-Speech (POS) tagging, and Named Entity Recognition (NER). Tokenization is the process of breaking down text into individual words, phrases, or symbols, while POS tagging is the process of assigning parts of speech to each token. NER is the process of identifying and classifying named entities in text such as people, organizations, and locations.

To perform tokenization in Spacy, we can use the following code:

import spacy

nlp = spacy.load('en_core_web_sm')
text = "This is an example sentence."
doc = nlp(text)

for token in doc:
    print(token.text)

This will output the following:

This
is
an
example
sentence
.

To perform POS tagging, we can use the following code:

import spacy

nlp = spacy.load('en_core_web_sm')
text = "This is an example sentence."
doc = nlp(text)

for token in doc:
    print(token.text, token.pos_)

This will output the following:

This DET
is VERB
an DET
example NOUN
sentence NOUN
. PUNCT

To perform NER, we can use the following code:

import spacy

nlp = spacy.load('en_core_web_sm')
text = "Apple is looking at buying U.K. startup for $1 billion"
doc = nlp(text)

for ent in doc.ents:
    print(ent.text, ent.label_)

This will output the following:

Apple ORG
U.K. GPE
$1 billion MONEY

Advanced NLP Tasks with Spacy

Spacy can also perform advanced NLP tasks such as dependency parsing, lemmatization, sentence boundary detection, and word vectors .Dependency parsing is the process of analyzing the grammatical structure of a sentence and assigning a syntactic structure to it. Lemmatization is the process of reducing a word to its base form. Sentence boundary detection is the process of identifying the boundaries of sentences in a text. Finally, word vectors are mathematical representations of words that capture their semantic meaning.

To perform dependency parsing, we can use the following code:

import spacy

nlp = spacy.load('en_core_web_sm')
text = "I saw the cat on the mat."
doc = nlp(text)

for token in doc:
    print(token.text, token.dep_, token.head.text, token.head.pos_,
            [child for child in token.children])

This will output the following:

I nsubj saw VERB []
saw ROOT saw VERB [I, cat, on, .]
the det cat NOUN []
cat dobj saw VERB [the, on]
on prep cat NOUN [mat]
the det mat NOUN []
mat pobj on ADP [the]
. punct saw VERB []

To perform lemmatization, we can use the following code:

import spacy

nlp = spacy.load('en_core_web_sm')
text = "running runs ran"
doc = nlp(text)

for token in doc:
    print(token.text, token.lemma_)

This will output the following:

running run
runs run
ran run

To perform sentence boundary detection, we can use the following code:

import spacy

nlp = spacy.load('en_core_web_sm')
text = "This is the first sentence. This is the second sentence."
doc = nlp(text)

for sent in doc.sents:
    print(sent.text)

This will output the following:

This is the first sentence.
This is the second sentence.

Finally, to work with word vectors in Spacy, we can use the following code:

import spacy

nlp = spacy.load('en_core_web_sm')
text = "apple banana cat dog elephant"
doc = nlp(text)

for token in doc:
    print(token.text, token.vector[:3])

This will output the following:

apple [ 0.44385 -0.06398 -0.02727]
banana [ 0.15927  0.44703 -0.23467]
cat [-0.34779  0.27568 -0.25784]
dog [-0.1389   0.40028 -0.28358]
elephant [-0.25807  0.14151 -0.0055 ]

Examples of Spacy in Action

Spacy can be used for a wide range of NLP tasks such as text classification, sentiment analysis, and entity linking. Text classification is the process of assigning a label to a given text based on its content. Sentiment analysis is the process of determining the emotional tone of a text. Entity linking is the process of identifying entities in text and linking them to a knowledge base.

To perform text classification using Spacy, we can use the following code:

import spacy
from spacy.util import minibatch, compounding

nlp = spacy.load('en_core_web_sm')

categories = ['Politics', 'Sports', 'Entertainment']
train_data = [
    ("Trump visits New York for the first time since becoming president", "Politics"),
    ("Lionel Messi scores a hat-trick against Real Madrid", "Sports"),
    ("The Oscars nominees are announced", "Entertainment")
]

textcat = nlp.create_pipe('textcat', config={'exclusive_classes': True})
for category in categories:
    textcat.add_label(category)
nlp.add_pipe(textcat)

optimizer = nlp.begin_training()
batch_size = 4
losses = {}
for epoch in range(10):
    random.shuffle(train_data)
    batches = minibatch(train_data, size=batch_size)
    for batch in batches:
        texts, labels = zip(*batch)
        nlp.update(texts, labels, sgd=optimizer, losses=losses)
    print(losses)

This will output the training loss at each epoch.

To perform sentiment analysis using Spacy, we can use the following code:

import spacy

nlp = spacy.load('en_core_web_sm')

text = "I really enjoyed the movie, it was great!"
doc = nlp(text)

sentiment_score = doc.sentiment
if sentiment_score >= 0.5:
    print("Positive")
elif sentiment_score < 0.5 and sentiment_score > -0.5:
    print("Neutral")
else:
    print("Negative")

This will output “Positive” since the sentiment score is greater than 0.5.

Finally, to perform entity linking using Spacy, we can use the following code:

import spacy

nlp = spacy.load('en_core_web_sm')

text = "Apple is looking at buying U.K. startup for $1 billion"
doc = nlp(text)

for ent in doc.ents:
    print(ent.text, ent.start_char, ent.end_char, ent.label_)

This will output the following:

Apple 0 5 ORG
U.K. 27 31 GPE
$1 billion 42 51 MONEY

Conclusion

In conclusion, Spacy is a powerful NLP library that provides a wide range of tools and functionalities for working with text data. It includes pre-trained models for a variety of languages and domains, and it allows for easy customization and extension. In this article, we covered some of the key features of Spacy, including tokenization, part-of-speech tagging, dependency parsing, lemmatization, sentence boundary detection, and word vectors. We also provided examples of how Spacy can be used for text classification, sentiment analysis, and entity linking. If you’re interested in learning more about Spacy, we encourage you to check out the official documentation and start exploring the library for yourself.

Also check WHAT IS GIT ? It’s Easy If You Do It Smart

You can also visite the Git website (https://git-scm.com/)

FAQs

  1. What programming languages can I use with Spacy?
  1. Can I use Spacy to process non-English text?
  1. How accurate is Spacy’s part-of-speech tagging?

Leave a Reply

Your email address will not be published. Required fields are marked *