mirror of
https://github.com/explosion/spaCy.git
synced 2025-10-27 14:11:04 +03:00
* Restructure tag maps for MorphAnalysis changes
Prepare tag maps for upcoming MorphAnalysis changes that allow
arbritrary features.
* Use default tag map rather than duplicating for ca / uk / vi
* Import tag map into defaults for ga
* Modify tag maps so all morphological fields and features are strings
* Move features from `"Other"` to the top level
* Rewrite tuples as strings separated by `","`
* Rewrite morph symbols for fr lemmatizer as strings
* Export MorphAnalysis under spacy.tokens
* Modify morphology to support arbitrary features
Modify `Morphology` and `MorphAnalysis` so that arbitrary features are
supported.
* Modify `MorphAnalysisC` so that it can support arbitrary features and
multiple values per field. `MorphAnalysisC` is redesigned to contain:
* key: hash of UD FEATS string of morphological features
* array of `MorphFeatureC` structs that each contain a hash of `Field`
and `Field=Value` for a given morphological feature, which makes it
possible to:
* find features by field
* represent multiple values for a given field
* `get_field()` is renamed to `get_by_field()` and is no longer `nogil`.
Instead a new helper function `get_n_by_field()` is `nogil` and returns
`n` features by field.
* `MorphAnalysis.get()` returns all possible values for a field as a
list of individual features such as `["Tense=Pres", "Tense=Past"]`.
* `MorphAnalysis`'s `str()` and `repr()` are the UD FEATS string.
* `Morphology.feats_to_dict()` converts a UD FEATS string to a dict
where:
* Each field has one entry in the dict
* Multiple values remain separated by a separator in the value string
* `Token.morph_` returns the UD FEATS string and you can set
`Token.morph_` with a UD FEATS string or with a tag map dict.
* Modify get_by_field to use np.ndarray
Modify `get_by_field()` to use np.ndarray. Remove `max_results` from
`get_n_by_field()` and always iterate over all the fields.
* Rewrite without MorphFeatureC
* Add shortcut for existing feats strings as keys
Add shortcut for existing feats strings as keys in `Morphology.add()`.
* Check for '_' as empty analysis when adding morphs
* Extend helper converters in Morphology
Add and extend helper converters that convert and normalize between:
* UD FEATS strings (`"Case=dat,gen|Number=sing"`)
* per-field dict of feats (`{"Case": "dat,gen", "Number": "sing"}`)
* list of individual features (`["Case=dat", "Case=gen",
"Number=sing"]`)
All converters sort fields and values where applicable.
139 lines
5.0 KiB
Python
139 lines
5.0 KiB
Python
from ...lemmatizer import Lemmatizer
|
|
from ...symbols import POS, NOUN, VERB, ADJ, ADV, PRON, DET, AUX, PUNCT, ADP
|
|
from ...symbols import SCONJ, CCONJ
|
|
|
|
|
|
class FrenchLemmatizer(Lemmatizer):
|
|
"""
|
|
French language lemmatizer applies the default rule based lemmatization
|
|
procedure with some modifications for better French language support.
|
|
|
|
The parts of speech 'ADV', 'PRON', 'DET', 'ADP' and 'AUX' are added to use
|
|
the rule-based lemmatization. As a last resort, the lemmatizer checks in
|
|
the lookup table.
|
|
"""
|
|
|
|
def __call__(self, string, univ_pos, morphology=None):
|
|
lookup_table = self.lookups.get_table("lemma_lookup", {})
|
|
if "lemma_rules" not in self.lookups:
|
|
return [lookup_table.get(string, string)]
|
|
if univ_pos in (NOUN, "NOUN", "noun"):
|
|
univ_pos = "noun"
|
|
elif univ_pos in (VERB, "VERB", "verb"):
|
|
univ_pos = "verb"
|
|
elif univ_pos in (ADJ, "ADJ", "adj"):
|
|
univ_pos = "adj"
|
|
elif univ_pos in (ADP, "ADP", "adp"):
|
|
univ_pos = "adp"
|
|
elif univ_pos in (ADV, "ADV", "adv"):
|
|
univ_pos = "adv"
|
|
elif univ_pos in (AUX, "AUX", "aux"):
|
|
univ_pos = "aux"
|
|
elif univ_pos in (CCONJ, "CCONJ", "cconj"):
|
|
univ_pos = "cconj"
|
|
elif univ_pos in (DET, "DET", "det"):
|
|
univ_pos = "det"
|
|
elif univ_pos in (PRON, "PRON", "pron"):
|
|
univ_pos = "pron"
|
|
elif univ_pos in (PUNCT, "PUNCT", "punct"):
|
|
univ_pos = "punct"
|
|
elif univ_pos in (SCONJ, "SCONJ", "sconj"):
|
|
univ_pos = "sconj"
|
|
else:
|
|
return [self.lookup(string)]
|
|
# See Issue #435 for example of where this logic is requied.
|
|
if self.is_base_form(univ_pos, morphology):
|
|
return list(set([string.lower()]))
|
|
index_table = self.lookups.get_table("lemma_index", {})
|
|
exc_table = self.lookups.get_table("lemma_exc", {})
|
|
rules_table = self.lookups.get_table("lemma_rules", {})
|
|
lemmas = self.lemmatize(
|
|
string,
|
|
index_table.get(univ_pos, {}),
|
|
exc_table.get(univ_pos, {}),
|
|
rules_table.get(univ_pos, []),
|
|
)
|
|
return lemmas
|
|
|
|
def is_base_form(self, univ_pos, morphology=None):
|
|
"""
|
|
Check whether we're dealing with an uninflected paradigm, so we can
|
|
avoid lemmatization entirely.
|
|
"""
|
|
morphology = {} if morphology is None else morphology
|
|
others = [
|
|
key
|
|
for key in morphology
|
|
if key not in (POS, "Number", "POS", "VerbForm", "Tense")
|
|
]
|
|
if univ_pos == "noun" and morphology.get("Number") == "sing":
|
|
return True
|
|
elif univ_pos == "verb" and morphology.get("VerbForm") == "inf":
|
|
return True
|
|
# This maps 'VBP' to base form -- probably just need 'IS_BASE'
|
|
# morphology
|
|
elif univ_pos == "verb" and (
|
|
morphology.get("VerbForm") == "fin"
|
|
and morphology.get("Tense") == "pres"
|
|
and morphology.get("Number") is None
|
|
and not others
|
|
):
|
|
return True
|
|
elif univ_pos == "adj" and morphology.get("Degree") == "pos":
|
|
return True
|
|
elif "VerbForm=inf" in morphology:
|
|
return True
|
|
elif "VerbForm=none" in morphology:
|
|
return True
|
|
elif "Number=sing" in morphology:
|
|
return True
|
|
elif "Degree=pos" in morphology:
|
|
return True
|
|
else:
|
|
return False
|
|
|
|
def noun(self, string, morphology=None):
|
|
return self(string, "noun", morphology)
|
|
|
|
def verb(self, string, morphology=None):
|
|
return self(string, "verb", morphology)
|
|
|
|
def adj(self, string, morphology=None):
|
|
return self(string, "adj", morphology)
|
|
|
|
def punct(self, string, morphology=None):
|
|
return self(string, "punct", morphology)
|
|
|
|
def lookup(self, string, orth=None):
|
|
lookup_table = self.lookups.get_table("lemma_lookup", {})
|
|
if orth is not None and orth in lookup_table:
|
|
return lookup_table[orth][0]
|
|
return string
|
|
|
|
def lemmatize(self, string, index, exceptions, rules):
|
|
lookup_table = self.lookups.get_table("lemma_lookup", {})
|
|
string = string.lower()
|
|
forms = []
|
|
if string in index:
|
|
forms.append(string)
|
|
return forms
|
|
forms.extend(exceptions.get(string, []))
|
|
oov_forms = []
|
|
if not forms:
|
|
for old, new in rules:
|
|
if string.endswith(old):
|
|
form = string[: len(string) - len(old)] + new
|
|
if not form:
|
|
pass
|
|
elif form in index or not form.isalpha():
|
|
forms.append(form)
|
|
else:
|
|
oov_forms.append(form)
|
|
if not forms:
|
|
forms.extend(oov_forms)
|
|
if not forms and string in lookup_table.keys():
|
|
forms.append(lookup_table[string][0])
|
|
if not forms:
|
|
forms.append(string)
|
|
return list(set(forms))
|