SpaCy or NLTK?

When developing a language-learning app, natural language processing (NLP) can be very useful for handling tasks like tokenization, part-of-speech tagging, and named entity recognition. If your backend is in Python, the two most widely used NLP libraries are spaCy and NLTK.

Each has its strengths, but they cater to different needs. This post will help you determine which is best for your API by comparing the two libraries generally. And of course make sure to read the docs, any guides or other writings you can find, and to experiment with them yourself to make a better informed choice.

But first. If you are a developer used to working with Javascript, as I am, you might wonder if there are any JS libraries out there. I actually jumped straight to Python libraries without even considering investigating JS tools simply because I'm so used to hearing about Python and NLP together. But there are some javascript NLP tools available, for example natural and compromise. So why Use Python Instead of JavaScript NLP Libraries? Python is generally the better choice due to:

• Performance: Python’s NLP libraries, especially spaCy, are optimized for speed and efficiency.
• Pre-trained Models: spaCy and NLTK offer robust pre-trained models, making them more powerful than JavaScript alternatives.
• Stronger Community & Research Integration: Python dominates the AI and NLP space, ensuring better support and ongoing improvements.
• Scalability: Python integrates well with high-performance backends like FastAPI, making it ideal for API-based language-learning applications.

If you're building an NLP-powered language-learning app that requires fast and accurate text processing, Python's ecosystem is a better fit than JavaScript’s NLP tools.

So let's compare two of the battle tested libraries that are part of that ecosystem.

Key Differences Between spaCy and NLTK

Feature	spaCy 🏆 (Best for Production)	NLTK (Best for Prototyping & Research)
Ease of Use	Simple API, ready-to-use models	Requires more manual setup
Speed	Optimized in Cython, very fast	Slower due to pure Python implementation
Scalability	Designed for large-scale applications	More suitable for small-scale experiments
Tokenization	Rule-based and statistical, highly accurate	More flexible but requires configuration
POS Tagging	Pre-trained models with high accuracy	Needs manual setup with different taggers
Named Entity Recognition (NER)	High-quality built-in models	Requires additional training
Syntax Parsing	Built-in dependency parsing	Needs third-party libraries like stanfordnlp
Customization	Supports custom models & pipelines	More granular customization
Use Case	Best for production apps & APIs	Best for research and teaching

Some example implementations:

1.Named Entity Recognition (NER)

This example demonstrates how spaCy provides built-in NER models, whereas NLTK requires extra steps for entity recognition.

NLTK

1   import nltk
2   from nltk.tokenize import word_tokenize
3   from nltk import pos_tag, ne_chunk
4  
5   nltk.download('maxent_ne_chunker')
6   nltk.download('words')
7  
8   text = "Ada Lovelace wrote the first computer algorithm in 1843."
9   tokens = word_tokenize(text)
10  pos_tags = pos_tag(tokens)
11  ner_tree = ne_chunk(pos_tags)
12 
13  print(ner_tree)

#output (which requires parsing to get the named entities)
(S
  (PERSON Ada/NNP)
  (PERSON Lovelace/NNP)
  wrote/VBD
  the/DT
  first/JJ
  computer/NN
  algorithm/NN
  in/IN
  1843/CD
  ./.)

Spacy

1   import spacy
2   
3   nlp = spacy.load("en_core_web_sm")
4   doc = nlp("Ada Lovelace wrote the first computer algorithm in 1843.")
5   
6   print([(ent.text, ent.label_) for ent in doc.ents])

#outputs a nice python list.
[('Ada Lovelace', 'PERSON'), ('1843', 'DATE')]

2.Lemmatization

This example compares lemmatization in spaCy and NLTK, showing that spaCy provides a cleaner API.

NLTK

1    import nltk
2    from nltk.stem import WordNetLemmatizer
3    from nltk.tokenize import word_tokenize
4    from nltk.corpus import wordnet
5    from nltk import pos_tag
6    
7    nltk.download('wordnet')
8    nltk.download('omw-1.4')
9    nltk.download('punkt')
10   nltk.download('averaged_perceptron_tagger')
11   
12   lemmatizer = WordNetLemmatizer()
13   text = "The children are running in the park while the leaves were falling."
14   
15   tokens = word_tokenize(text)
16   pos_tags = pos_tag(tokens)
17   
18  # Convert NLTK POS tags to WordNet POS tags
19   def get_wordnet_pos(treebank_tag):
20       if treebank_tag.startswith('J'):
21           return wordnet.ADJ
22       elif treebank_tag.startswith('V'):
23           return wordnet.VERB
24       elif treebank_tag.startswith('N'):
25           return wordnet.NOUN
26       elif treebank_tag.startswith('R'):
27           return wordnet.ADV
28       else:
29           return wordnet.NOUN  # Default to noun
30   
31   lemmatized = [lemmatizer.lemmatize(word, get_wordnet_pos(tag)) for word, tag in pos_tags]
32   print(lemmatized)

#output
['The', 'child', 'be', 'run', 'in', 'the', 'park', 'while', 'the', 'leaf', 'be', 'fall', '.']

Spacy

1   import spacy
2   
3   nlp = spacy.load("en_core_web_sm")
4   doc = nlp("The children are running in the park while the leaves were falling.")
5   
6   lemmatized = [token.lemma_ for token in doc]
7   print(lemmatized)

#output
['the', 'child', 'be', 'run', 'in', 'the', 'park', 'while', 'the', 'leaf', 'be', 'fall', '.']

Scalability Considerations

When building a language-learning app, the ability to handle large amounts of text efficiently is important. Here’s how spaCy and NLTK compare in terms of scalability:

1. Performance and Processing Speed: SpaCy is designed for large-scale applications and processes text much faster than NLTK because it is implemented in Cython (a C-optimized Python). I don't really know what that is but I understand it is why it is a faster library. NLTK, on the other hand, processes each step independently, making it slower and less efficient for real-time applications.

2. Memory Usage: SpaCy loads large pre-trained models into memory, allowing for fast processing of text. Though I believe this could create performance issues depending on how it is implemented in your project... I'm still learning about this! NLTK operates step by step, making it more memory-efficient for small-scale tasks but significantly slower for high-volume processing.

3. Real time functionality: Spacy is optimized for high traffic API use and real time interactions (so better for uses in things like chatbots). NLTK may not be as good at handling those sort of tasks on it's own.

Final Recommendation

If you're building a production-ready API for a language-learning app, choose spaCy for better performance, scalability, and ease of use. If you're experimenting with NLP concepts or working on linguistic research, NLTK provides greater flexibility. And of course there's no reason you can't play around with both.