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117 lines
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ReStructuredText
117 lines
5.2 KiB
ReStructuredText
====================
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Annotation Standards
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====================
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This document describes the target annotations spaCy is trained to predict.
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This is currently a work in progress. Please ask questions on the issue tracker,
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so that the answers can be integrated here to improve the documentation.
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https://github.com/honnibal/spaCy/issues
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English
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=======
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Tokenization
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------------
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Tokenization standards are based on the OntoNotes 5 corpus.
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The tokenizer differs from most by including tokens for significant whitespace.
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Any sequence of whitespace characters beyond a single space (' ') is included
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as a token. For instance:
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>>> from spacy.en import English
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>>> nlp = English(parse=False)
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>>> tokens = nlp(u'Some\nspaces and\ttab characters')
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>>> print [t.orth_ for t in tokens]
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[u'Some', u'\n', u'spaces', u' ', u'and', u'\t', u'tab', u'characters']
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The whitespace tokens are useful for much the same reason punctuation is --- it's
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often an important delimiter in the text. By preserving it in the token output,
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we are able to maintain a simple alignment between the tokens and the original
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string, and we ensure that the token stream does not lose information.
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Sentence boundary detection
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---------------------------
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Sentence boundaries are calculated from the syntactic parse tree, so features
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such as punctuation and capitalisation play an important but non-decisive role
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in determining the sentence boundaries. Usually this means that the sentence
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boundaries will at least coincide with clause boundaries, even given poorly
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punctuated text.
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Part-of-speech Tagging
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----------------------
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The part-of-speech tagger uses the OntoNotes 5 version of the Penn Treebank
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tag set. We also map the tags to the simpler Google Universal POS Tag set.
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Details here: https://github.com/honnibal/spaCy/blob/master/spacy/en/pos.pyx#L124
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Lemmatization
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-------------
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A "lemma" is the uninflected form of a word. In English, this means:
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* Adjectives: The form like "happy", not "happier" or "happiest"
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* Adverbs: The form like "badly", not "worse" or "worst"
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* Nouns: The form like "dog", not "dogs"; like "child", not "children"
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* Verbs: The form like "write", not "writes", "writing", "wrote" or "written"
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The lemmatization data is taken from WordNet. However, we also add a special
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case for pronouns: all pronouns are lemmatized to the special token -PRON-.
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Syntactic Dependency Parsing
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----------------------------
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The parser is trained on data produced by the ClearNLP converter. Details of
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the annotation scheme can be found here:
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http://www.mathcs.emory.edu/~choi/doc/clear-dependency-2012.pdf
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Named Entity Recognition
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------------------------
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+--------------+-----------------------------------------------------+
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| PERSON | People, including fictional |
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+--------------+-----------------------------------------------------+
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| NORP | Nationalities or religious or political groups |
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+--------------+-----------------------------------------------------+
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| FACILITY | Buildings, airports, highways, bridges, etc. |
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+--------------+-----------------------------------------------------+
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| ORGANIZATION | Companies, agencies, institutions, etc. |
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+--------------+-----------------------------------------------------+
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| GPE | Countries, cities, states |
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+--------------+-----------------------------------------------------+
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| LOCATION | Non-GPE locations, mountain ranges, bodies of water |
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+--------------+-----------------------------------------------------+
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| PRODUCT | Vehicles, weapons, foods, etc. (Not services) |
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+--------------+-----------------------------------------------------+
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| EVENT | Named hurricanes, battles, wars, sports events, etc.|
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+--------------+-----------------------------------------------------+
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| WORK OF ART | Titles of books, songs, etc. |
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+--------------+-----------------------------------------------------+
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| LAW | Named documents made into laws |
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+--------------+-----------------------------------------------------+
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| LANGUAGE | Any named language |
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+--------------+-----------------------------------------------------+
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The following values are also annotated in a style similar to names:
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+--------------+---------------------------------------------+
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| DATE | Absolute or relative dates or periods |
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+--------------+---------------------------------------------+
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| TIME | Times smaller than a day |
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+--------------+---------------------------------------------+
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| PERCENT | Percentage (including “%”) |
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+--------------+---------------------------------------------+
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| MONEY | Monetary values, including unit |
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+--------------+---------------------------------------------+
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| QUANTITY | Measurements, as of weight or distance |
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+--------------+---------------------------------------------+
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| ORDINAL | "first", "second" |
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+--------------+---------------------------------------------+
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| CARDINAL | Numerals that do not fall under another type|
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+--------------+---------------------------------------------+
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