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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 icon in ["pro", "pro", "con", "pro"]
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+cell.u-text-center #[+procon(icon)]
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+row
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+cell Integrated word vectors
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each icon in ["pro", "con", "con", "con"]
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+cell.u-text-center #[+procon(icon)]
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+row
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+cell Multi-language support
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each icon in ["pro", "pro", "pro", "pro"]
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+cell.u-text-center #[+procon(icon)]
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+row
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+cell Tokenization
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each icon in ["pro", "pro", "pro", "pro"]
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+cell.u-text-center #[+procon(icon)]
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+row
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+cell Part-of-speech tagging
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each icon in ["pro", "pro", "pro", "pro"]
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+cell.u-text-center #[+procon(icon)]
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+row
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+cell Sentence segmentation
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each icon in ["pro", "pro", "pro", "pro"]
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+cell.u-text-center #[+procon(icon)]
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+row
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+cell Dependency parsing
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each icon in ["pro", "pro", "con", "pro"]
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+cell.u-text-center #[+procon(icon)]
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+row
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+cell Entity recognition
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each icon in ["pro", "con", "pro", "pro"]
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+cell.u-text-center #[+procon(icon)]
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+row
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+cell Coreference resolution
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each icon in ["con", "con", "con", "pro"]
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+cell.u-text-center #[+procon(icon)]
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