Spacy Vs NLTK

Spacy:

SpaCy is an open-source library for advanced Natural Language Processing (NLP) in Python. It is designed specifically for production use and provides efficient, reliable, and easy-to-use NLP tools. SpaCy supports tokenization, part-of-speech tagging, named entity recognition, dependency parsing, and many other NLP tasks.

1. Feature:

• Features
• Support for 75+ languages
• 84 trained pipelines for 25 languages
• Multi-task learning with pretrained transformers like BERT
• Pretrained word vectors
• State-of-the-art speed
• Production-ready training system
• Linguistically-motivated tokenization
• Components for named entity recognition, part-of-speech tagging, dependency parsing, sentence segmentation, text classification, lemmatization, morphological analysis, entity linking and more
• Easily extensible with custom components and attributes
• Support for custom models in PyTorch, TensorFlow and other frameworks
• Built in visualizers for syntax and NER
• Easy model packaging, deployment and workflow management
• Robust, rigorously evaluated accuracy.

Installation

pip install spacy
#for ubuntu
python3 -m spacy download en_core_web_sm
#for windows
python -m spacy download en_core_web_sm

NLTK(The Natural Language Toolkit)

It is a powerful Python library for working with human language data (text). It provides easy-to-use interfaces to over 50 corpora and lexical resources such as WordNet, along with a suite of text-processing libraries for classification, tokenization, stemming, tagging, parsing, and semantic reasoning. It also includes wrappers for industrial-strength NLP libraries.

Installation

pip install nltk
import nltk
nltk.download('all')

This will download all the datasets and models that NLTK uses. You might only need to download certain datasets for specific tasks, which I'll show in examples.

Tokenization

• Spacy

import spacy

# Load the English language model
nlp = spacy.load("en_core_web_sm")

# Process the text
text = "Hello there! How are you doing today? The weather is great, and Python is awesome."
doc = nlp(text)

# Sentence tokenization
sentences = list(doc.sents)
print("Sentences:", [sent.text for sent in sentences])

# Word tokenization
words = [token.text for token in doc]
print("Words:", words)

• NLTK

import nltk
from nltk.tokenize import word_tokenize, sent_tokenize

text = "Hello there! How are you doing today? The weather is great, and Python is awesome."

# Sentence tokenization
sentences = sent_tokenize(text)
print("Sentences:", sentences)

# Word tokenization
words = word_tokenize(text)
print("Words:", words)

Part-of-Speech Tagging

• NLTK

import nltk
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag

text = "NLTK is a leading platform for building Python programs to work with human language data."
words = word_tokenize(text)
pos_tags = pos_tag(words)
print("POS Tags:", pos_tags)

• SpaCy

import spacy

# Load the English language model
nlp = spacy.load("en_core_web_sm")

# Process the text
text = "spaCy is a free, open-source library for advanced Natural Language Processing (NLP) in Python."
doc = nlp(text)
pos_tags = [(token.text, token.pos_, token.tag_) for token in doc]
print("POS Tags:", pos_tags)

Named Entity Recognition (NER)

• NLTK

import nltk
from nltk.tokenize import word_tokenize
from nltk.tag import pos_tag
from nltk.chunk import ne_chunk

text = "Barack Obama was born in Hawaii. He was elected president in 2008."
words = word_tokenize(text)
pos_tags = pos_tag(words)
named_entities = ne_chunk(pos_tags)
print("Named Entities:", named_entities)

• SpaCy

import spacy

# Load the English language model
nlp = spacy.load("en_core_web_sm")

# Process the text
text = "Barack Obama was born in Hawaii. He was elected president in 2008."
doc = nlp(text)
named_entities = [(ent.text, ent.label_) for ent in doc.ents]
print("Named Entities:", named_entities)

Performance

NLTK:

• Generally slower due to its design for educational purposes and comprehensive nature.
• More flexible and provides access to various algorithms.

SpaCy:

• Optimized for performance and speed.
• Faster processing, making it suitable for real-time applications and large-scale processing.

Ease of Use

NLTK:

• More complex and requires more code to achieve certain tasks.

• Great for learning and understanding the intricacies of NLP.

SpaCy:

• Simple and consistent API.

• Designed to be user-friendly and quick to implement.

Resources and Datasets

NLTK:

• Comes with numerous corpora and datasets.

• Provides access to various lexical resources like WordNet.

spaCy:

• Does not include as many built-in datasets.

• Focuses on practical tools and pre-trained models for immediate use.

Customization and Extensibility

NLTK:

• Highly customizable and allows for experimenting with different algorithms.

• Good for research and exploring new techniques.

SpaCy:

• Extensible with custom pipelines and components.

• Designed for practical application and integration into larger systems.

When to Use NLTK

Educational purposes: If you are learning NLP and want to understand the underlying algorithms and techniques.
Research: When you need to experiment with different NLP models and algorithms.

When to Use spaCy

Production use: When you need a reliable and fast NLP library for real-world applications.
Ease of use: When you want to implement NLP tasks quickly and efficiently.

Conclusion

NLTK is excellent for educational purposes, research, and when you need a wide variety of tools and datasets.
spaCy is ideal for production environments, real-time applications, and when you need a fast, efficient, and easy-to-use library.