Need advice about which tool to choose?Ask the StackShare community!

NLTK

128
178
+ 1
0
SpaCy

218
297
+ 1
14
Add tool

NLTK vs SpaCy: What are the differences?

Key Differences between NLTK and SpaCy

Natural Language Toolkit (NLTK) and SpaCy are two popular libraries used for natural language processing (NLP) tasks, but they have some key differences:

  1. Tokenization: NLTK uses regular expression-based tokenization methods, which may lead to inaccurate results for complex tokenization tasks. On the other hand, SpaCy utilizes a rule-based approach for tokenization, which results in more accurate and efficient tokenization.

  2. Part-of-Speech (POS) Tagging: NLTK provides a wide variety of POS taggers, ranging from rule-based to machine learning-based taggers. SpaCy, on the other hand, uses a deep learning-based approach for POS tagging, resulting in higher accuracy. SpaCy also offers pre-trained models for POS tagging in various languages.

  3. Dependency Parsing: NLTK has multiple dependency parsing algorithms, including both rule-based and machine learning-based approaches. SpaCy's dependency parsing, on the other hand, is solely based on deep learning techniques, making it more accurate and efficient.

  4. Named Entity Recognition (NER): NLTK provides various NER algorithms, including rule-based and statistical approaches. SpaCy, on the other hand, offers a highly efficient and accurate transformer-based NER model for detecting entities such as names, organizations, and dates.

  5. Performance: SpaCy is known for its efficient processing speed, thanks to its optimized, low-level implementation in Cython. NLTK, on the other hand, can be slower for certain tasks due to its Python implementation.

  6. User-Friendliness: SpaCy is designed to have a more user-friendly API, making it easier to use and understand. NLTK, on the other hand, has a steeper learning curve and may require more code to achieve similar tasks.

In summary, SpaCy offers more efficient and accurate tokenization, POS tagging, dependency parsing, and NER, while NLTK provides a wider range of algorithms and tools but may require more effort and code to achieve similar results.

Manage your open source components, licenses, and vulnerabilities
Learn More
Pros of NLTK
Pros of SpaCy
    Be the first to leave a pro
    • 12
      Speed
    • 2
      No vendor lock-in

    Sign up to add or upvote prosMake informed product decisions

    Cons of NLTK
    Cons of SpaCy
      Be the first to leave a con
      • 1
        Requires creating a training set and managing training

      Sign up to add or upvote consMake informed product decisions

      - No public GitHub repository available -

      What is NLTK?

      It is a suite of libraries and programs for symbolic and statistical natural language processing for English written in the Python programming language.

      What is SpaCy?

      It is a library for advanced Natural Language Processing in Python and Cython. It's built on the very latest research, and was designed from day one to be used in real products. It comes with pre-trained statistical models and word vectors, and currently supports tokenization for 49+ languages.

      Need advice about which tool to choose?Ask the StackShare community!

      What companies use NLTK?
      What companies use SpaCy?
      Manage your open source components, licenses, and vulnerabilities
      Learn More

      Sign up to get full access to all the companiesMake informed product decisions

      What tools integrate with NLTK?
      What tools integrate with SpaCy?
      What are some alternatives to NLTK and SpaCy?
      Gensim
      It is a Python library for topic modelling, document indexing and similarity retrieval with large corpora. Target audience is the natural language processing (NLP) and information retrieval (IR) community.
      TensorFlow
      TensorFlow is an open source software library for numerical computation using data flow graphs. Nodes in the graph represent mathematical operations, while the graph edges represent the multidimensional data arrays (tensors) communicated between them. The flexible architecture allows you to deploy computation to one or more CPUs or GPUs in a desktop, server, or mobile device with a single API.
      PyTorch
      PyTorch is not a Python binding into a monolothic C++ framework. It is built to be deeply integrated into Python. You can use it naturally like you would use numpy / scipy / scikit-learn etc.
      scikit-learn
      scikit-learn is a Python module for machine learning built on top of SciPy and distributed under the 3-Clause BSD license.
      Keras
      Deep Learning library for Python. Convnets, recurrent neural networks, and more. Runs on TensorFlow or Theano. https://keras.io/
      See all alternatives