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4a4f9666b2
Use distinctive icons instead of only colour, add proper handling of labels (hidden or visible, but always present) with optional custom text.
59 lines
1.8 KiB
Plaintext
59 lines
1.8 KiB
Plaintext
//- 💫 DOCS > USAGE > FACTS & FIGURES > FEATURE COMPARISON
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p
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| Here's a quick comparison of the functionalities offered by spaCy,
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| #[+a("https://github.com/tensorflow/models/tree/master/research/syntaxnet") SyntaxNet],
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| #[+a("http://www.nltk.org/py-modindex.html") NLTK] and
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| #[+a("http://stanfordnlp.github.io/CoreNLP/") CoreNLP].
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+table(["", "spaCy", "SyntaxNet", "NLTK", "CoreNLP"])
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+row
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+cell Programming language
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each lang in ["Python", "C++", "Python", "Java"]
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+cell.u-text-small.u-text-center=lang
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+row
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+cell Neural network models
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each answer in ["yes", "yes", "no", "yes"]
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+cell.u-text-center #[+procon(answer)]
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+row
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+cell Integrated word vectors
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each answer in ["yes", "no", "no", "no"]
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+cell.u-text-center #[+procon(answer)]
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+row
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+cell Multi-language support
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each answer in ["yes", "yes", "yes", "yes"]
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+cell.u-text-center #[+procon(answer)]
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+row
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+cell Tokenization
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each answer in ["yes", "yes", "yes", "yes"]
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+cell.u-text-center #[+procon(answer)]
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+row
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+cell Part-of-speech tagging
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each answer in ["yes", "yes", "yes", "yes"]
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+cell.u-text-center #[+procon(answer)]
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+row
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+cell Sentence segmentation
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each answer in ["yes", "yes", "yes", "yes"]
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+cell.u-text-center #[+procon(answer)]
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+row
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+cell Dependency parsing
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each answer in ["yes", "yes", "no", "yes"]
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+cell.u-text-center #[+procon(answer)]
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+row
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+cell Entity recognition
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each answer in ["yes", "no", "yes", "yes"]
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+cell.u-text-center #[+procon(answer)]
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+row
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+cell Coreference resolution
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each answer in ["no", "no", "no", "yes"]
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+cell.u-text-center #[+procon(answer)]
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