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💫 Industrial-strength Natural Language Processing (NLP) in Python
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Matthew Honnibal 590f38bdb2 * Add hacky solution to Issue #220. Currently specials.json only supports literal patterns, which doesn't allow us to pre-tag whitespace with the correct token, SP, as a rule. The data-driven approach should be easy but for some reason fails here. Adding a hard code in Morphology isn't a good solution, but we do want to fix the behaviour right away, and don't want to wait for an architecturally better solution. 2016-01-19 03:35:20 +01:00
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corpora/en * Add wordnet 2015-09-21 19:06:48 +10:00
examples * Fix sentence iteration bug in pos_tag example 2015-12-05 20:25:12 +01:00
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spacy * Add hacky solution to Issue #220. Currently specials.json only supports literal patterns, which doesn't allow us to pre-tag whitespace with the correct token, SP, as a rule. The data-driven approach should be easy but for some reason fails here. Adding a hard code in Morphology isn't a good solution, but we do want to fix the behaviour right away, and don't want to wait for an architecturally better solution. 2016-01-19 03:35:20 +01:00
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README.md

Travis CI status Appveyor status

spaCy: Industrial-strength NLP

spaCy is a library for advanced natural language processing in Python and Cython.

Documentation and details: http://spacy.io/

spaCy is built on the very latest research, but it isn't researchware. It was designed from day 1 to be used in real products. It's commercial open-source software, released under the MIT license.

Features

  • Labelled dependency parsing (91.8% accuracy on OntoNotes 5)
  • Named entity recognition (82.6% accuracy on OntoNotes 5)
  • Part-of-speech tagging (97.1% accuracy on OntoNotes 5)
  • Easy to use word vectors
  • All strings mapped to integer IDs
  • Export to numpy data arrays
  • Alignment maintained to original string, ensuring easy mark up calculation
  • Range of easy-to-use orthographic features.
  • No pre-processing required. spaCy takes raw text as input, warts and newlines and all.

Top Peformance

  • Fastest in the world: <50ms per document. No faster system has ever been announced.
  • Accuracy within 1% of the current state of the art on all tasks performed (parsing, named entity recognition, part-of-speech tagging). The only more accurate systems are an order of magnitude slower or more.

Supports

  • CPython 2.7
  • CPython 3.4
  • CPython 3.5
  • OSX
  • Linux
  • Cygwin
  • Visual Studio

Difficult to support:

  • PyPy 2.7
  • PyPy 3.4