Practical Applications of spaCy in Data Science | by Harshita Aswani … – Medium

spaCy is a powerful and efficient Python library for natural language processing (NLP). It provides pre-trained models, efficient tokenization, part-of-speech tagging, named entity recognition, and much more. In this blog post, we will explore the practical applications of spaCy and demonstrate how it can simplify and enhance your NLP tasks.

Before we dive into the examples, lets install spaCy using pip, the Python package installer.

spaCy provides a simple and intuitive API for performing common NLP tasks. Lets consider an example of using spaCy to perform tokenization, part-of-speech tagging, and named entity recognition (NER) on a text.

# Load the English language modelnlp = spacy.load('en_core_web_sm')

# Process a texttext = "Apple Inc. is planning to open a new store in New York City."doc = nlp(text)

# Perform tokenizationtokens = [token.text for token in doc]print("Tokens:", tokens)

# Perform part-of-speech taggingpos_tags = [(token.text, token.pos_) for token in doc]print("POS Tags:", pos_tags)

# Perform named entity recognitionentities = [(entity.text, entity.label_) for entity in doc.ents]print("Named Entities:", entities)

spaCy allows you to customize and train your own models for specific NLP tasks. This includes training entity recognition models, part-of-speech taggers, and more. Lets consider an example of training a named entity recognition model using spaCy.

# Load the base English language modelnlp = spacy.load('en_core_web_sm')

# Define training datatrain_data = [(u"Apple Inc. is planning to open a new store in New York City.", {"entities": [(0, 10, "ORG"), (45, 58, "GPE")]}),# Add more training examples here]

# Define and initialize a new NER pipelinener = nlp.get_pipe("ner")

# Add labels to the NER pipelinener.add_label("ORG")ner.add_label("GPE")

# Train the NER modelfor epoch in range(10):for text, annotations in train_data:doc = nlp.make_doc(text)example = Example.from_dict(doc, annotations)nlp.update([example], losses={ner: 1.0})

# Save the trained modelnlp.to_disk("trained_model")

# Load the trained modelnlp_loaded = spacy.load("trained_model")

# Process a text with the trained modeltext = "Apple Inc. is planning to open a new store in New York City."doc = nlp_loaded(text)

# Perform named entity recognition with the trained modelentities = [(entity.text, entity.label_) for entity in doc.ents]print("Named Entities:", entities)

spaCy is a powerful and user-friendly library for natural language processing tasks. In this blog post, we explored the practical applications of spaCy, including tokenization, part-of-speech tagging, and named entity recognition. We also demonstrated how to train a custom named entity recognition model using spaCy.

With spaCy, you can process and analyze text data with ease, perform advanced NLP tasks, and even train your own models for specific domains or applications.

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Practical Applications of spaCy in Data Science | by Harshita Aswani ... - Medium