Spacy Vs NLTK
Tags: python, machinelearning, nlp, programming
Published: 2024-06-02 13:04:41

```

```
## 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.