Introduction to Libraries of NLP in Python — NLTK vs. spaCy

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The two significant libraries used in NLP are NLTK and spaCy. There are substantial differences between them, which are as follows:
  1. NLTK provides a plethora of algorithms to choose from for a particular problem which is a boon for a researcher but a bane for a developer. Whereas, spaCy keeps the best algorithm for a problem in its toolkit and keep it updated as state of the art improves.
  2. NLTK supports various languages whereas spaCy have statistical models for 7 languages (English, German, Spanish, French, Portuguese, Italian, and Dutch). It also supports named entities for multi-language.
  3. NLTK is a string processing library. It takes strings as input and returns strings or lists of strings as output. Whereas, spaCy uses an object-oriented approach. When we parse a text, spaCy returns document object whose words and sentences are objects themselves.
  4. spaCy has support for word vectors whereas NLTK does not.
  5. As spaCy uses the latest and best algorithms, its performance is usually good as compared to NLTK. As we can see below, in word tokenization and POS-tagging spaCy performs better, but in sentence tokenization, NLTK outperforms spaCy. Its poor performance in sentence tokenization is a result of differing approaches: NLTK attempts to split the text into sentences. In contrast, spaCy constructs a syntactic tree for each sentence, a more robust method that yields much more information about the text.

The picture was taken from Google image search

PS. will add info about Gensim soon.

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Akanksha Malhotra

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