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spaCy/website/docs/api/index.jade

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//- 💫 DOCS > API > FACTS & FIGURES
include ../../_includes/_mixins
+h(2, "comparison") Feature comparison
p
| Here's a quick comparison of the functionalities offered by spaCy,
| #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet],
| #[+a("http://www.nltk.org/py-modindex.html") NLTK] and
| #[+a("http://stanfordnlp.github.io/CoreNLP/") CoreNLP].
+table([ "", "spaCy", "SyntaxNet", "NLTK", "CoreNLP"])
+row
+cell Easy installation
each icon in [ "pro", "con", "pro", "pro" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell Python API
each icon in [ "pro", "con", "pro", "con" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell Multi-language support
each icon in [ "neutral", "pro", "pro", "pro" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell Tokenization
each icon in [ "pro", "pro", "pro", "pro" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell Part-of-speech tagging
each icon in [ "pro", "pro", "pro", "pro" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell Sentence segmentation
each icon in [ "pro", "pro", "pro", "pro" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell Dependency parsing
each icon in [ "pro", "pro", "con", "pro" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell Entity Recognition
each icon in [ "pro", "con", "pro", "pro" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell Integrated word vectors
each icon in [ "pro", "con", "con", "con" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell Sentiment analysis
each icon in [ "pro", "con", "pro", "pro" ]
+cell.u-text-center #[+procon(icon)]
+row
+cell Coreference resolution
each icon in [ "con", "con", "con", "pro" ]
+cell.u-text-center #[+procon(icon)]
+h(2, "benchmarks") Benchmarks
p
| Two peer-reviewed papers in 2015 confirm that spaCy offers the
| #[strong fastest syntactic parser in the world] and that
| #[strong its accuracy is within 1% of the best] available. The few
| systems that are more accurate are 20× slower or more.
+aside("About the evaluation")
| The first of the evaluations was published by #[strong Yahoo! Labs] and
| #[strong Emory University], as part of a survey of current parsing
| technologies #[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") (Choi et al., 2015)].
| Their results and subsequent discussions helped us develop a novel
| psychologically-motivated technique to improve spaCy's accuracy, which
| we published in joint work with Macquarie University
| #[+a("https://aclweb.org/anthology/D/D15/D15-1162.pdf") (Honnibal and Johnson, 2015)].
+table([ "System", "Language", "Accuracy", "Speed (wps)"])
+row
each data in [ "spaCy", "Cython", "91.8", "13,963" ]
+cell #[strong=data]
+row
each data in [ "ClearNLP", "Java", "91.7", "10,271" ]
+cell=data
+row
each data in [ "CoreNLP", "Java", "89.6", "8,602"]
+cell=data
+row
each data in [ "MATE", "Java", "92.5", "550"]
+cell=data
+row
each data in [ "Turbo", "C++", "92.4", "349" ]
+cell=data
+h(3, "parse-accuracy") Parse accuracy
p
| In 2016, Google released their
| #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") SyntaxNet]
| library, setting a new state of the art for syntactic dependency parsing
| accuracy. SyntaxNet's algorithm is very similar to spaCy's. The main
| difference is that SyntaxNet uses a neural network while spaCy uses a
| sparse linear model.
+aside("Methodology")
| #[+a("http://arxiv.org/abs/1603.06042") Andor et al. (2016)] chose
| slightly different experimental conditions from
| #[+a("https://aclweb.org/anthology/P/P15/P15-1038.pdf") Choi et al. (2015)],
| so the two accuracy tables here do not present directly comparable
| figures. We have only evaluated spaCy in the "News" condition following
| the SyntaxNet methodology. We don't yet have benchmark figures for the
| "Web" and "Questions" conditions.
+table([ "System", "News", "Web", "Questions" ])
+row
+cell spaCy
each data in [ 92.8, "n/a", "n/a" ]
+cell=data
+row
+cell #[+a("https://github.com/tensorflow/models/tree/master/syntaxnet") Parsey McParseface]
each data in [ 94.15, 89.08, 94.77 ]
+cell=data
+row
+cell #[+a("http://www.cs.cmu.edu/~ark/TurboParser/") Martins et al. (2013)]
each data in [ 93.10, 88.23, 94.21 ]
+cell=data
+row
+cell #[+a("http://research.google.com/pubs/archive/38148.pdf") Zhang and McDonald (2014)]
each data in [ 93.32, 88.65, 93.37 ]
+cell=data
+row
+cell #[+a("http://static.googleusercontent.com/media/research.google.com/en//pubs/archive/43800.pdf") Weiss et al. (2015)]
each data in [ 93.91, 89.29, 94.17 ]
+cell=data
+row
+cell #[strong #[+a("http://arxiv.org/abs/1603.06042") Andor et al. (2016)]]
each data in [ 94.44, 90.17, 95.40 ]
+cell #[strong=data]
+h(3, "speed-comparison") Detailed speed comparison
p
| Here we compare the per-document processing time of various spaCy
| functionalities against other NLP libraries. We show both absolute
| timings (in ms) and relative performance (normalized to spaCy). Lower is
| better.
+aside("Methodology")
| #[strong Set up:] 100,000 plain-text documents were streamed from an
| SQLite3 database, and processed with an NLP library, to one of three
| levels of detail — tokenization, tagging, or parsing. The tasks are
| additive: to parse the text you have to tokenize and tag it. The
| pre-processing was not subtracted from the times — I report the time
| required for the pipeline to complete. I report mean times per document,
| in milliseconds.#[br]#[br]
| #[strong Hardware]: Intel i7-3770 (2012)#[br]
| #[strong Implementation]: #[+src(gh("spacy-benchmarks")) spacy-benchmarks]
+table
+row.u-text-label.u-text-center
th.c-table__head-cell
th.c-table__head-cell(colspan="3") Absolute (ms per doc)
th.c-table__head-cell(colspan="3") Relative (to spaCy)
+row
each column in ["System", "Tokenize", "Tag", "Parse", "Tokenize", "Tag", "Parse"]
th.c-table__head-cell.u-text-label=column
+row
+cell #[strong spaCy]
each data in [ "0.2ms", "1ms", "19ms"]
+cell #[strong=data]
each data in [ "1x", "1x", "1x" ]
+cell=data
+row
each data in [ "CoreNLP", "2ms", "10ms", "49ms", "10x", "10x", "2.6x"]
+cell=data
+row
each data in [ "ZPar", "1ms", "8ms", "850ms", "5x", "8x", "44.7x" ]
+cell=data
+row
each data in [ "NLTK", "4ms", "443ms", "n/a", "20x", "443x", "n/a" ]
+cell=data
+h(3, "ner") Named entity comparison
p
| #[+a("https://aclweb.org/anthology/W/W16/W16-2703.pdf") Jiang et al. (2016)]
| present several detailed comparisons of the named entity recognition
| models provided by spaCy, CoreNLP, NLTK and LingPipe. Here we show their
| evaluation of person, location and organization accuracy on Wikipedia.
+aside("Methodology")
| Making a meaningful comparison of different named entity recognition
| systems is tricky. Systems are often trained on different data, which
| usually have slight differences in annotation style. For instance, some
| corpora include titles as part of person names, while others don't.
| These trivial differences in convention can distort comparisons
| significantly. Jiang et al.'s #[em partial overlap] metric goes a long
| way to solving this problem.
+table([ "System", "Precision", "Recall", "F-measure" ])
+row
+cell spaCy
each data in [ 0.7240, 0.6514, 0.6858 ]
+cell=data
+row
+cell #[strong CoreNLP]
each data in [ 0.7914, 0.7327, 0.7609 ]
+cell #[strong=data]
+row
+cell NLTK
each data in [ 0.5136, 0.6532, 0.5750 ]
+cell=data
+row
+cell LingPipe
each data in [ 0.5412, 0.5357, 0.5384 ]
+cell=data
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