Though this seems like a simple way to use the library, in practice, you’ll often find yourself going back to the documentation to discover new functions. NLTK is essentially a string processing library, where each function takes strings as input and returns a processed string. For an app builder mindset that prioritizes getting features done, spaCy would be the better choice.Ī core difference between NLTK and spaCy stems from the way in which these libraries were built. It also offers access to larger word vectors that are easier to customize. It provides the fastest and most accurate syntactic analysis of any NLP library released to date. While NLTK provides access to many algorithms to get something done, spaCy provides the best way to do it. SpaCy, on the other hand, is the way to go for app developers. After all, NLTK was created to support education and help students explore ideas. Its modules are easy to build on and it doesn’t really abstract away any functionality. For scholars and researchers who want to build something from the ground up or provide a functioning model of their thesis, NLTK is the way to go. In contrast, spaCy is similar to a service: it helps you get specific tasks done.ĭue to this difference, NLTK and spaCy are better suited for different types of developers. It almost acts as a toolbox of NLP algorithms. NLTK was built by scholars and researchers as a tool to help you create complex NLP functions. There’s a real philosophical difference between NLTK and spaCy. First, I’ll provide some background about how NLTK and spaCy differ in general, and then I’ll dive into how each library handles specific tasks.
While both can theoretically accomplish any NLP task, each one excels in certain scenarios. You can build chatbots, automatic summarizers, and entity extraction engines with either of these libraries. NLTK and spaCy are two of the most popular Natural Language Processing (NLP) tools available in Python.
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