spaCy/spacy/lang/fr/lemmatizer.py

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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
Modify morphology to support arbitrary features (#4932) * 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.
2020-01-24 00:01:54 +03:00
elif "VerbForm=inf" in morphology:
return True
Modify morphology to support arbitrary features (#4932) * 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.
2020-01-24 00:01:54 +03:00
elif "VerbForm=none" in morphology:
return True
Modify morphology to support arbitrary features (#4932) * 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.
2020-01-24 00:01:54 +03:00
elif "Number=sing" in morphology:
return True
Modify morphology to support arbitrary features (#4932) * 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.
2020-01-24 00:01:54 +03:00
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))