spaCy/website/usage/_facts-figures/_feature-comparison.jade

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//- 💫 DOCS > USAGE > FACTS & FIGURES > FEATURE COMPARISON
p
| 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
| #[+a("http://stanfordnlp.github.io/CoreNLP/") CoreNLP].
+table(["", "spaCy", "SyntaxNet", "NLTK", "CoreNLP"])
+row
+cell Programming language
each lang in ["Python", "C++", "Python", "Java"]
+cell.u-text-small.u-text-center=lang
+row
+cell Neural network models
each answer in ["yes", "yes", "no", "yes"]
+cell.u-text-center #[+procon(answer)]
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+row
+cell Integrated word vectors
each answer in ["yes", "no", "no", "no"]
+cell.u-text-center #[+procon(answer)]
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+row
+cell Multi-language support
each answer in ["yes", "yes", "yes", "yes"]
+cell.u-text-center #[+procon(answer)]
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+row
+cell Tokenization
each answer in ["yes", "yes", "yes", "yes"]
+cell.u-text-center #[+procon(answer)]
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+row
+cell Part-of-speech tagging
each answer in ["yes", "yes", "yes", "yes"]
+cell.u-text-center #[+procon(answer)]
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+row
+cell Sentence segmentation
each answer in ["yes", "yes", "yes", "yes"]
+cell.u-text-center #[+procon(answer)]
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+row
+cell Dependency parsing
each answer in ["yes", "yes", "no", "yes"]
+cell.u-text-center #[+procon(answer)]
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+row
+cell Entity recognition
each answer in ["yes", "no", "yes", "yes"]
+cell.u-text-center #[+procon(answer)]
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+row
+cell Coreference resolution
each answer in ["no", "no", "no", "yes"]
+cell.u-text-center #[+procon(answer)]