spaCy/website/docs/api/annotation.md

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Annotation Specifications	Schemes used for labels, tags and training data	Text Processing text-processing POS Tagging pos-tagging Dependencies dependency-parsing Named Entities named-entities Models & Training training

Text processing

Example

from spacy.lang.en import English
nlp = English()
tokens = nlp("Some\\nspaces  and\\ttab characters")
tokens_text = [t.text for t in tokens]
assert tokens_text == ["Some", "\\n", "spaces", " ", "and", "\\t", "tab", "characters"]

Tokenization standards are based on the OntoNotes 5 corpus. The tokenizer differs from most by including tokens for significant whitespace. Any sequence of whitespace characters beyond a single space (' ') is included as a token. The whitespace tokens are useful for much the same reason punctuation is – it's often an important delimiter in the text. By preserving it in the token output, we are able to maintain a simple alignment between the tokens and the original string, and we ensure that no information is lost during processing.

Lemmatization

Examples

In English, this means:

• Adjectives: happier, happiest → happy
• Adverbs: worse, worst → badly
• Nouns: dogs, children → dog, child
• Verbs: writes, writing, wrote, written → write

As of v2.2, lemmatization data is stored in a separate package, spacy-lookups-data that can be installed if needed via pip install spacy[lookups]. Some languages provide full lemmatization rules and exceptions, while other languages currently only rely on simple lookup tables.

spaCy adds a special case for English pronouns: all English pronouns are lemmatized to the special token -PRON-. Unlike verbs and common nouns, there's no clear base form of a personal pronoun. Should the lemma of "me" be "I", or should we normalize person as well, giving "it" — or maybe "he"? spaCy's solution is to introduce a novel symbol, -PRON-, which is used as the lemma for all personal pronouns.

Sentence boundary detection

Sentence boundaries are calculated from the syntactic parse tree, so features such as punctuation and capitalization play an important but non-decisive role in determining the sentence boundaries. Usually this means that the sentence boundaries will at least coincide with clause boundaries, even given poorly punctuated text.

Part-of-speech tagging

Tip: Understanding tags

You can also use spacy.explain to get the description for the string representation of a tag. For example, spacy.explain("RB") will return "adverb".

This section lists the fine-grained and coarse-grained part-of-speech tags assigned by spaCy's models. The individual mapping is specific to the training corpus and can be defined in the respective language data's tag_map.py.

spaCy maps all language-specific part-of-speech tags to a small, fixed set of word type tags following the Universal Dependencies scheme. The universal tags don't code for any morphological features and only cover the word type. They're available as the Token.pos and Token.pos_ attributes.

POS	Description	Examples
ADJ	adjective	big, old, green, incomprehensible, first
ADP	adposition	in, to, during
ADV	adverb	very, tomorrow, down, where, there
AUX	auxiliary	is, has (done), will (do), should (do)
CONJ	conjunction	and, or, but
CCONJ	coordinating conjunction	and, or, but
DET	determiner	a, an, the
INTJ	interjection	psst, ouch, bravo, hello
NOUN	noun	girl, cat, tree, air, beauty
NUM	numeral	1, 2017, one, seventy-seven, IV, MMXIV
PART	particle	's, not,
PRON	pronoun	I, you, he, she, myself, themselves, somebody
PROPN	proper noun	Mary, John, London, NATO, HBO
PUNCT	punctuation	., (, ), ?
SCONJ	subordinating conjunction	if, while, that
SYM	symbol	$, %, §, ©, +, −, ×, ÷, =, :), 😝
VERB	verb	run, runs, running, eat, ate, eating
X	other	sfpksdpsxmsa
SPACE	space

The English part-of-speech tagger uses the OntoNotes 5 version of the Penn Treebank tag set. We also map the tags to the simpler Universal Dependencies v2 POS tag set.

Tag	POS	Morphology	Description
$	SYM		symbol, currency
``	PUNCT	PunctType=quot PunctSide=ini	opening quotation mark
''	PUNCT	PunctType=quot PunctSide=fin	closing quotation mark
,	PUNCT	PunctType=comm	punctuation mark, comma
-LRB-	PUNCT	PunctType=brck PunctSide=ini	left round bracket
-RRB-	PUNCT	PunctType=brck PunctSide=fin	right round bracket
.	PUNCT	PunctType=peri	punctuation mark, sentence closer
:	PUNCT		punctuation mark, colon or ellipsis
ADD	X		email
AFX	ADJ	Hyph=yes	affix
CC	CCONJ	ConjType=comp	conjunction, coordinating
CD	NUM	NumType=card	cardinal number
DT	DET		determiner
EX	PRON	AdvType=ex	existential there
FW	X	Foreign=yes	foreign word
GW	X		additional word in multi-word expression
HYPH	PUNCT	PunctType=dash	punctuation mark, hyphen
IN	ADP		conjunction, subordinating or preposition
JJ	ADJ	Degree=pos	adjective
JJR	ADJ	Degree=comp	adjective, comparative
JJS	ADJ	Degree=sup	adjective, superlative
LS	X	NumType=ord	list item marker
MD	VERB	VerbType=mod	verb, modal auxiliary
NFP	PUNCT		superfluous punctuation
NIL	X		missing tag
NN	NOUN	Number=sing	noun, singular or mass
NNP	PROPN	NounType=prop Number=sing	noun, proper singular
NNPS	PROPN	NounType=prop Number=plur	noun, proper plural
NNS	NOUN	Number=plur	noun, plural
PDT	DET		predeterminer
POS	PART	Poss=yes	possessive ending
PRP	PRON	PronType=prs	pronoun, personal
PRP$	DET	PronType=prs Poss=yes	pronoun, possessive
RB	ADV	Degree=pos	adverb
RBR	ADV	Degree=comp	adverb, comparative
RBS	ADV	Degree=sup	adverb, superlative
RP	ADP		adverb, particle
SP	SPACE		space
SYM	SYM		symbol
TO	PART	PartType=inf VerbForm=inf	infinitival "to"
UH	INTJ		interjection
VB	VERB	VerbForm=inf	verb, base form
VBD	VERB	VerbForm=fin Tense=past	verb, past tense
VBG	VERB	VerbForm=part Tense=pres Aspect=prog	verb, gerund or present participle
VBN	VERB	VerbForm=part Tense=past Aspect=perf	verb, past participle
VBP	VERB	VerbForm=fin Tense=pres	verb, non-3rd person singular present
VBZ	VERB	VerbForm=fin Tense=pres Number=sing Person=three	verb, 3rd person singular present
WDT	DET		wh-determiner
WP	PRON		wh-pronoun, personal
WP$	DET	Poss=yes	wh-pronoun, possessive
WRB	ADV		wh-adverb
XX	X		unknown
_SP	SPACE

The German part-of-speech tagger uses the TIGER Treebank annotation scheme. We also map the tags to the simpler Universal Dependencies v2 POS tag set.

Tag	POS	Morphology	Description
$(	PUNCT	PunctType=brck	other sentence-internal punctuation mark
$,	PUNCT	PunctType=comm	comma
$.	PUNCT	PunctType=peri	sentence-final punctuation mark
ADJA	ADJ		adjective, attributive
ADJD	ADJ		adjective, adverbial or predicative
ADV	ADV		adverb
APPO	ADP	AdpType=post	postposition
APPR	ADP	AdpType=prep	preposition; circumposition left
APPRART	ADP	AdpType=prep PronType=art	preposition with article
APZR	ADP	AdpType=circ	circumposition right
ART	DET	PronType=art	definite or indefinite article
CARD	NUM	NumType=card	cardinal number
FM	X	Foreign=yes	foreign language material
ITJ	INTJ		interjection
KOKOM	CCONJ	ConjType=comp	comparative conjunction
KON	CCONJ		coordinate conjunction
KOUI	SCONJ		subordinate conjunction with "zu" and infinitive
KOUS	SCONJ		subordinate conjunction with sentence
NE	PROPN		proper noun
NN	NOUN		noun, singular or mass
NNE	PROPN		proper noun
PDAT	DET	PronType=dem	attributive demonstrative pronoun
PDS	PRON	PronType=dem	substituting demonstrative pronoun
PIAT	DET	`PronType=ind	neg
PIS	PRON	`PronType=ind	neg
PPER	PRON	PronType=prs	replaceable personal pronoun
PPOSAT	DET	Poss=yes PronType=prs	attributive possessive pronoun
PPOSS	PRON	Poss=yes PronType=prs	substituting possessive pronoun
PRELAT	DET	PronType=rel	attributive relative pronoun
PRELS	PRON	PronType=rel	substituting relative pronoun
PRF	PRON	PronType=prs Reflex=yes	reflexive personal pronoun
PROAV	ADV	PronType=dem	pronominal adverb
PTKA	PART		particle with adjective or adverb
PTKANT	PART	PartType=res	answer particle
PTKNEG	PART	Polarity=neg	negative particle
PTKVZ	ADP	PartType=vbp	separable verbal particle
PTKZU	PART	PartType=inf	"zu" before infinitive
PWAT	DET	PronType=int	attributive interrogative pronoun
PWAV	ADV	PronType=int	adverbial interrogative or relative pronoun
PWS	PRON	PronType=int	substituting interrogative pronoun
TRUNC	X	Hyph=yes	word remnant
VAFIN	AUX	Mood=ind VerbForm=fin	finite verb, auxiliary
VAIMP	AUX	Mood=imp VerbForm=fin	imperative, auxiliary
VAINF	AUX	VerbForm=inf	infinitive, auxiliary
VAPP	AUX	Aspect=perf VerbForm=part	perfect participle, auxiliary
VMFIN	VERB	Mood=ind VerbForm=fin VerbType=mod	finite verb, modal
VMINF	VERB	VerbForm=inf VerbType=mod	infinitive, modal
VMPP	VERB	Aspect=perf VerbForm=part VerbType=mod	perfect participle, modal
VVFIN	VERB	Mood=ind VerbForm=fin	finite verb, full
VVIMP	VERB	Mood=imp VerbForm=fin	imperative, full
VVINF	VERB	VerbForm=inf	infinitive, full
VVIZU	VERB	VerbForm=inf	infinitive with "zu", full
VVPP	VERB	Aspect=perf VerbForm=part	perfect participle, full
XY	X		non-word containing non-letter
_SP	SPACE

---

For the label schemes used by the other models, see the respective tag_map.py in spacy/lang.

Syntactic Dependency Parsing

Tip: Understanding labels

You can also use spacy.explain to get the description for the string representation of a label. For example, spacy.explain("prt") will return "particle".

This section lists the syntactic dependency labels assigned by spaCy's models. The individual labels are language-specific and depend on the training corpus.

The Universal Dependencies scheme is used in all languages trained on Universal Dependency Corpora.

Label	Description
acl	clausal modifier of noun (adjectival clause)
advcl	adverbial clause modifier
advmod	adverbial modifier
amod	adjectival modifier
appos	appositional modifier
aux	auxiliary
case	case marking
cc	coordinating conjunction
ccomp	clausal complement
clf	classifier
compound	compound
conj	conjunct
cop	copula
csubj	clausal subject
dep	unspecified dependency
det	determiner
discourse	discourse element
dislocated	dislocated elements
expl	expletive
fixed	fixed multiword expression
flat	flat multiword expression
goeswith	goes with
iobj	indirect object
list	list
mark	marker
nmod	nominal modifier
nsubj	nominal subject
nummod	numeric modifier
obj	object
obl	oblique nominal
orphan	orphan
parataxis	parataxis
punct	punctuation
reparandum	overridden disfluency
root	root
vocative	vocative
xcomp	open clausal complement

The English dependency labels use the CLEAR Style by ClearNLP.

Label	Description
acl	clausal modifier of noun (adjectival clause)
acomp	adjectival complement
advcl	adverbial clause modifier
advmod	adverbial modifier
agent	agent
amod	adjectival modifier
appos	appositional modifier
attr	attribute
aux	auxiliary
auxpass	auxiliary (passive)
case	case marking
cc	coordinating conjunction
ccomp	clausal complement
compound	compound
conj	conjunct
cop	copula
csubj	clausal subject
csubjpass	clausal subject (passive)
dative	dative
dep	unclassified dependent
det	determiner
dobj	direct object
expl	expletive
intj	interjection
mark	marker
meta	meta modifier
neg	negation modifier
nn	noun compound modifier
nounmod	modifier of nominal
npmod	noun phrase as adverbial modifier
nsubj	nominal subject
nsubjpass	nominal subject (passive)
nummod	numeric modifier
oprd	object predicate
obj	object
obl	oblique nominal
parataxis	parataxis
pcomp	complement of preposition
pobj	object of preposition
poss	possession modifier
preconj	pre-correlative conjunction
prep	prepositional modifier
prt	particle
punct	punctuation
quantmod	modifier of quantifier
relcl	relative clause modifier
root	root
xcomp	open clausal complement

The German dependency labels use the TIGER Treebank annotation scheme.

Label	Description
ac	adpositional case marker
adc	adjective component
ag	genitive attribute
ams	measure argument of adjective
app	apposition
avc	adverbial phrase component
cc	comparative complement
cd	coordinating conjunction
cj	conjunct
cm	comparative conjunction
cp	complementizer
cvc	collocational verb construction
da	dative
dm	discourse marker
ep	expletive es
ju	junctor
mnr	postnominal modifier
mo	modifier
ng	negation
nk	noun kernel element
nmc	numerical component
oa	accusative object
oa2	second accusative object
oc	clausal object
og	genitive object
op	prepositional object
par	parenthetical element
pd	predicate
pg	phrasal genitive
ph	placeholder
pm	morphological particle
pnc	proper noun component
punct	punctuation
rc	relative clause
re	repeated element
rs	reported speech
sb	subject
sbp	passivized subject (PP)
sp	subject or predicate
svp	separable verb prefix
uc	unit component
vo	vocative
ROOT	root

Named Entity Recognition

Tip: Understanding entity types

You can also use spacy.explain to get the description for the string representation of an entity label. For example, spacy.explain("LANGUAGE") will return "any named language".

Models trained on the OntoNotes 5 corpus support the following entity types:

Type	Description
PERSON	People, including fictional.
NORP	Nationalities or religious or political groups.
FAC	Buildings, airports, highways, bridges, etc.
ORG	Companies, agencies, institutions, etc.
GPE	Countries, cities, states.
LOC	Non-GPE locations, mountain ranges, bodies of water.
PRODUCT	Objects, vehicles, foods, etc. (Not services.)
EVENT	Named hurricanes, battles, wars, sports events, etc.
WORK_OF_ART	Titles of books, songs, etc.
LAW	Named documents made into laws.
LANGUAGE	Any named language.
DATE	Absolute or relative dates or periods.
TIME	Times smaller than a day.
PERCENT	Percentage, including "%".
MONEY	Monetary values, including unit.
QUANTITY	Measurements, as of weight or distance.
ORDINAL	"first", "second", etc.
CARDINAL	Numerals that do not fall under another type.

Wikipedia scheme

Models trained on Wikipedia corpus (Nothman et al., 2013) use a less fine-grained NER annotation scheme and recognise the following entities:

Type	Description
PER	Named person or family.
LOC	Name of politically or geographically defined location (cities, provinces, countries, international regions, bodies of water, mountains).
ORG	Named corporate, governmental, or other organizational entity.
MISC	Miscellaneous entities, e.g. events, nationalities, products or works of art.

IOB Scheme

Tag	ID	Description
"I"	1	Token is inside an entity.
"O"	2	Token is outside an entity.
"B"	3	Token begins an entity.
""	0	No entity tag is set (missing value).

BILUO Scheme

Tag	Description
**B**EGIN	The first token of a multi-token entity.
**I**N	An inner token of a multi-token entity.
**L**AST	The final token of a multi-token entity.
**U**NIT	A single-token entity.
**O**UT	A non-entity token.

Why BILUO, not IOB?

There are several coding schemes for encoding entity annotations as token tags. These coding schemes are equally expressive, but not necessarily equally learnable. Ratinov and Roth showed that the minimal Begin, In, Out scheme was more difficult to learn than the BILUO scheme that we use, which explicitly marks boundary tokens.

spaCy translates the character offsets into this scheme, in order to decide the cost of each action given the current state of the entity recognizer. The costs are then used to calculate the gradient of the loss, to train the model. The exact algorithm is a pastiche of well-known methods, and is not currently described in any single publication. The model is a greedy transition-based parser guided by a linear model whose weights are learned using the averaged perceptron loss, via the dynamic oracle imitation learning strategy. The transition system is equivalent to the BILUO tagging scheme.

Models and training data

JSON input format for training

spaCy takes training data in JSON format. The built-in convert command helps you convert the .conllu format used by the Universal Dependencies corpora to spaCy's training format. To convert one or more existing Doc objects to spaCy's JSON format, you can use the gold.docs_to_json helper.

Annotating entities

Named entities are provided in the BILUO notation. Tokens outside an entity are set to "O" and tokens that are part of an entity are set to the entity label, prefixed by the BILUO marker. For example "B-ORG" describes the first token of a multi-token ORG entity and "U-PERSON" a single token representing a PERSON entity. The biluo_tags_from_offsets function can help you convert entity offsets to the right format.

### Example structure
[{
    "id": int,                      # ID of the document within the corpus
    "paragraphs": [{                # list of paragraphs in the corpus
        "raw": string,              # raw text of the paragraph
        "sentences": [{             # list of sentences in the paragraph
            "tokens": [{            # list of tokens in the sentence
                "id": int,          # index of the token in the document
                "dep": string,      # dependency label
                "head": int,        # offset of token head relative to token index
                "tag": string,      # part-of-speech tag
                "orth": string,     # verbatim text of the token
                "ner": string       # BILUO label, e.g. "O" or "B-ORG"
            }],
            "brackets": [{          # phrase structure (NOT USED by current models)
                "first": int,       # index of first token
                "last": int,        # index of last token
                "label": string     # phrase label
            }]
        }],
        "cats": [{                  # new in v2.2: categories for text classifier
            "label": string,        # text category label
            "value": float / bool   # label applies (1.0/true) or not (0.0/false)
        }]
    }]
}]

Here's an example of dependencies, part-of-speech tags and names entities, taken from the English Wall Street Journal portion of the Penn Treebank:

https://github.com/explosion/spaCy/tree/master/examples/training/training-data.json

Lexical data for vocabulary

To populate a model's vocabulary, you can use the spacy init-model command and load in a newline-delimited JSON (JSONL) file containing one lexical entry per line via the --jsonl-loc option. The first line defines the language and vocabulary settings. All other lines are expected to be JSON objects describing an individual lexeme. The lexical attributes will be then set as attributes on spaCy's Lexeme object. The vocab command outputs a ready-to-use spaCy model with a Vocab containing the lexical data.

### First line
{"lang": "en", "settings": {"oov_prob": -20.502029418945312}}

### Entry structure
{
    "orth": string,     # the word text
    "id": int,          # can correspond to row in vectors table
    "lower": string,
    "norm": string,
    "shape": string
    "prefix": string,
    "suffix": string,
    "length": int,
    "cluster": string,
    "prob": float,
    "is_alpha": bool,
    "is_ascii": bool,
    "is_digit": bool,
    "is_lower": bool,
    "is_punct": bool,
    "is_space": bool,
    "is_title": bool,
    "is_upper": bool,
    "like_url": bool,
    "like_num": bool,
    "like_email": bool,
    "is_stop": bool,
    "is_oov": bool,
    "is_quote": bool,
    "is_left_punct": bool,
    "is_right_punct": bool
}

Here's an example of the 20 most frequent lexemes in the English training data:

https://github.com/explosion/spaCy/tree/master/examples/training/vocab-data.jsonl