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What are the advantages of using different tokenizers? For example, let's take the sentence: "In Düsseldorf I took my hat off. But I can't put it back on."

The treebank tokenizer yields: "In Düsseldorf I took my hat off . But I ca n't put it back on . "

However, the whitespace tokenizer would yield: "In Düsseldorf I took my hat off . But I can't put it back on . "

NLTK has four tokenizers:

  • TreebankWordTokenizer
  • WordPunctTokenizer
  • PunctWordTokenizer
  • WhitespaceTokenizer

When should you use which one? For my project, I am interested in text generation, so I am leaning toward the whitespace tokenizer. Is this a good choice? Won't my model generate nonsense tokens like "n't" when I use eg the treebank tokenizer?

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  • $\begingroup$ comment as an answer due to insufficient karma. I would suggest trying all four and seeing how the results look. $\endgroup$
    – user70889
    Oct 5 at 13:12

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The problem is of text generation. I am assuming you are trying for chatbot etc where input is a natural lanugae and output is a natural language.

Since input is a natural language, all punctuations,special characters are important. For eg: Triple dot also means " to follow up" or "waiting". A tokenizer based on "." will remove this information.

Next step is to choose tokenizer which preserves punctuations. Tokenizer based on white space will do.

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  • $\begingroup$ Thank you for tha answer and your reasoning. I was also asking because a previous paper author used the treebank tokenizer to preprocess their training data which confused me. Creating a vocabulary based on nonsense tokens like "n't" seemed problematic to me. This reinforces my hunch that a whitespace tokenizer is the better choice indeed. $\endgroup$ Oct 6 at 18:50

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