mirror of
https://github.com/explosion/spaCy.git
synced 2024-11-14 13:47:13 +03:00
257 lines
11 KiB
ReStructuredText
257 lines
11 KiB
ReStructuredText
===
|
|
API
|
|
===
|
|
|
|
|
|
.. autoclass:: spacy.en.English
|
|
|
|
+------------+----------------------------------------+-------------+--------------------------+
|
|
| Attribute | Type | Attr API | Notes |
|
|
+============+========================================+=============+==========================+
|
|
| strings | :py:class:`strings.StringStore` | __getitem__ | string <-> int mapping |
|
|
+------------+----------------------------------------+-------------+--------------------------+
|
|
| vocab | :py:class:`vocab.Vocab` | __getitem__ | Look up Lexeme object |
|
|
+------------+----------------------------------------+-------------+--------------------------+
|
|
| tokenizer | :py:class:`tokenizer.Tokenizer` | __call__ | Get Tokens given unicode |
|
|
+------------+----------------------------------------+-------------+--------------------------+
|
|
| tagger | :py:class:`en.pos.EnPosTagger` | __call__ | Set POS tags on Tokens |
|
|
+------------+----------------------------------------+-------------+--------------------------+
|
|
| parser | :py:class:`syntax.parser.GreedyParser` | __call__ | Set parse on Tokens |
|
|
+------------+----------------------------------------+-------------+--------------------------+
|
|
| entity | :py:class:`syntax.parser.GreedyParser` | __call__ | Set entities on Tokens |
|
|
+------------+----------------------------------------+-------------+--------------------------+
|
|
| mwe_merger | :py:class:`multi_words.RegexMerger` | __call__ | Apply regex for units |
|
|
+------------+----------------------------------------+-------------+--------------------------+
|
|
|
|
|
|
.. automethod:: spacy.en.English.__call__
|
|
|
|
|
|
.. autoclass:: spacy.tokens.Tokens
|
|
:members:
|
|
|
|
+---------------+-------------+-------------+
|
|
| Attribute | Type | Attr API |
|
|
+===============+=============+=============+
|
|
| vocab | Vocab | __getitem__ |
|
|
+---------------+-------------+-------------+
|
|
| vocab.strings | StringStore | __getitem__ |
|
|
+---------------+-------------+-------------+
|
|
|
|
|
|
Internals
|
|
A Tokens instance stores the annotations in a C-array of `TokenC` structs.
|
|
Each TokenC struct holds a const pointer to a LexemeC struct, which describes
|
|
a vocabulary item.
|
|
|
|
The Token objects are built lazily, from this underlying C-data.
|
|
|
|
For faster access, the underlying C data can be accessed from Cython. You
|
|
can also export the data to a numpy array, via `Tokens.to_array`, if pure Python
|
|
access is required, and you need slightly better performance. However, this
|
|
is both slower and has a worse API than Cython access.
|
|
|
|
|
|
.. autoclass:: spacy.tokens.Token
|
|
|
|
Integer IDs are provided for all string features. The (unicode) string is
|
|
provided by an attribute of the same name followed by an underscore, e.g.
|
|
token.orth is an integer ID, token.orth\_ is the unicode value.
|
|
|
|
The only exception is the Token.string attribute, which is (unicode)
|
|
string-typed.
|
|
|
|
+--------------------------------------------------------------------------------+
|
|
| **Context-independent Attributes** (calculated once per entry in vocab) |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| Attribute | Type | Attribute | Type |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| orth/orth\_ | int/unicode | __len__ | int |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| lower/lower\_ | int/unicode | cluster | int |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| norm/norm\_ | int/unicode | prob | float |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| shape/shape\_ | int/unicode | repvec | ndarray(shape=(300,), dtype=float) |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| prefix/prefix\_ | int/unicode | | |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| suffix/suffix\_ | int/unicode | | |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| **Context-dependent Attributes** (calculated once per token in input) |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| Attribute | Type | Attribute | Type |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| whitespace\_ | unicode | string | unicode |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| pos/pos\_ | int/unicode | dep/dep\_ | int/unicode |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| tag/tag\_ | int/unicode | | |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
| lemma/lemma\_ | int/unicode | | |
|
|
+-----------------+-------------+-----------+------------------------------------+
|
|
|
|
**String Features**
|
|
|
|
string
|
|
The form of the word as it appears in the string, include trailing
|
|
whitespace. This is useful when you need to use linguistic features to
|
|
add inline mark-up to the string.
|
|
|
|
orth
|
|
The form of the word with no string normalization or processing, as it
|
|
appears in the string, without trailing whitespace.
|
|
|
|
lemma
|
|
The "base" of the word, with no inflectional suffixes, e.g. the lemma of
|
|
"developing" is "develop", the lemma of "geese" is "goose", etc. Note that
|
|
*derivational* suffixes are not stripped, e.g. the lemma of "instutitions"
|
|
is "institution", not "institute". Lemmatization is performed using the
|
|
WordNet data, but extended to also cover closed-class words such as
|
|
pronouns. By default, the WN lemmatizer returns "hi" as the lemma of "his".
|
|
We assign pronouns the lemma -PRON-.
|
|
|
|
lower
|
|
The form of the word, but forced to lower-case, i.e. lower = word.orth\_.lower()
|
|
|
|
norm
|
|
The form of the word, after language-specific normalizations have been
|
|
applied.
|
|
|
|
shape
|
|
A transform of the word's string, to show orthographic features. The
|
|
characters a-z are mapped to x, A-Z is mapped to X, 0-9 is mapped to d.
|
|
After these mappings, sequences of 4 or more of the same character are
|
|
truncated to length 4. Examples: C3Po --> XdXx, favorite --> xxxx,
|
|
:) --> :)
|
|
|
|
prefix
|
|
A length-N substring from the start of the word. Length may vary by
|
|
language; currently for English n=1, i.e. prefix = word.orth\_[:1]
|
|
|
|
suffix
|
|
A length-N substring from the end of the word. Length may vary by
|
|
language; currently for English n=3, i.e. suffix = word.orth\_[-3:]
|
|
|
|
**Distributional Features**
|
|
|
|
prob
|
|
The unigram log-probability of the word, estimated from counts from a
|
|
large corpus, smoothed using Simple Good Turing estimation.
|
|
|
|
cluster
|
|
The Brown cluster ID of the word. These are often useful features for
|
|
linear models. If you're using a non-linear model, particularly
|
|
a neural net or random forest, consider using the real-valued word
|
|
representation vector, in Token.repvec, instead.
|
|
|
|
repvec
|
|
A "word embedding" representation: a dense real-valued vector that supports
|
|
similarity queries between words. By default, spaCy currently loads
|
|
vectors produced by the Levy and Goldberg (2014) dependency-based word2vec
|
|
model.
|
|
|
|
**Syntactic Features**
|
|
|
|
tag
|
|
A morphosyntactic tag, e.g. NN, VBZ, DT, etc. These tags are
|
|
language/corpus specific, and typically describe part-of-speech and some
|
|
amount of morphological information. For instance, in the Penn Treebank
|
|
tag set, VBZ is assigned to a present-tense singular verb.
|
|
|
|
pos
|
|
A part-of-speech tag, from the Google Universal Tag Set, e.g. NOUN, VERB,
|
|
ADV. Constants for the 17 tag values are provided in spacy.parts\_of\_speech.
|
|
|
|
dep
|
|
The type of syntactic dependency relation between the word and its
|
|
syntactic head.
|
|
|
|
n_lefts
|
|
The number of immediate syntactic children preceding the word in the
|
|
string.
|
|
|
|
n_rights
|
|
The number of immediate syntactic children following the word in the
|
|
string.
|
|
|
|
**Navigating the Dependency Tree**
|
|
|
|
head
|
|
The Token that is the immediate syntactic head of the word. If the word is
|
|
the root of the dependency tree, the same word is returned.
|
|
|
|
lefts
|
|
An iterator for the immediate leftward syntactic children of the word.
|
|
|
|
rights
|
|
An iterator for the immediate rightward syntactic children of the word.
|
|
|
|
children
|
|
An iterator that yields from lefts, and then yields from rights.
|
|
|
|
subtree
|
|
An iterator for the part of the sentence syntactically governed by the
|
|
word, including the word itself.
|
|
|
|
|
|
**Named Entities**
|
|
|
|
ent_type
|
|
If the token is part of an entity, its entity type
|
|
|
|
ent_iob
|
|
The IOB (inside, outside, begin) entity recognition tag for the token
|
|
|
|
.. py:class:: vocab.Vocab(self, data_dir=None, lex_props_getter=None)
|
|
|
|
.. py:method:: __len__(self) --> int
|
|
|
|
.. py:method:: __getitem__(self, id: int) --> unicode
|
|
|
|
.. py:method:: __getitem__(self, string: unicode) --> int
|
|
|
|
.. py:method:: __setitem__(self, py_str: unicode, props: Dict[str, int[float]) --> None
|
|
|
|
.. py:method:: dump(self, loc: unicode) --> None
|
|
|
|
.. py:method:: load_lexemes(self, loc: unicode) --> None
|
|
|
|
.. py:method:: load_vectors(self, loc: unicode) --> None
|
|
|
|
.. py:class:: strings.StringStore(self)
|
|
|
|
.. py:method:: __len__(self) --> int
|
|
|
|
.. py:method:: __getitem__(self, id: int) --> unicode
|
|
|
|
.. py:method:: __getitem__(self, string: bytes) --> id
|
|
|
|
.. py:method:: __getitem__(self, string: unicode) --> id
|
|
|
|
.. py:method:: dump(self, loc: unicode) --> None
|
|
|
|
.. py:method:: load(self, loc: unicode) --> None
|
|
|
|
.. py:class:: tokenizer.Tokenizer(self, Vocab vocab, rules, prefix_re, suffix_re, infix_re, pos_tags, tag_names)
|
|
|
|
.. py:method:: tokens_from_list(self, List[unicode]) --> spacy.tokens.Tokens
|
|
|
|
.. py:method:: __call__(self, string: unicode) --> spacy.tokens.Tokens)
|
|
|
|
.. py:attribute:: vocab: spacy.vocab.Vocab
|
|
|
|
.. py:class:: en.pos.EnPosTagger(self, strings: spacy.strings.StringStore, data_dir: unicode)
|
|
|
|
.. py:method:: __call__(self, tokens: spacy.tokens.Tokens)
|
|
|
|
.. py:method:: train(self, tokens: spacy.tokens.Tokens, List[int] golds) --> int
|
|
|
|
.. py:method:: load_morph_exceptions(self, exc: Dict[unicode, Dict])
|
|
|
|
.. py:class:: syntax.parser.GreedyParser(self, model_dir: unicode)
|
|
|
|
.. py:method:: __call__(self, tokens: spacy.tokens.Tokens) --> None
|
|
|
|
.. py:method:: train(self, spacy.tokens.Tokens) --> None
|