spaCy/spacy/lang/uk/lemmatizer.py
2019-02-27 16:37:03 +01:00

208 lines
6.6 KiB
Python

# coding: utf8
from ...symbols import ADJ, DET, NOUN, NUM, PRON, PROPN, PUNCT, VERB, POS
from ...lemmatizer import Lemmatizer
class UkrainianLemmatizer(Lemmatizer):
_morph = None
def __init__(self):
super(UkrainianLemmatizer, self).__init__()
try:
from pymorphy2 import MorphAnalyzer
if UkrainianLemmatizer._morph is None:
UkrainianLemmatizer._morph = MorphAnalyzer(lang="uk")
except (ImportError, TypeError):
raise ImportError(
"The Ukrainian lemmatizer requires the pymorphy2 library and "
'dictionaries: try to fix it with "pip uninstall pymorphy2" and'
'"pip install git+https://github.com/kmike/pymorphy2.git pymorphy2-dicts-uk"'
)
def __call__(self, string, univ_pos, morphology=None):
univ_pos = self.normalize_univ_pos(univ_pos)
if univ_pos == "PUNCT":
return [PUNCT_RULES.get(string, string)]
if univ_pos not in ("ADJ", "DET", "NOUN", "NUM", "PRON", "PROPN", "VERB"):
# Skip unchangeable pos
return [string.lower()]
analyses = self._morph.parse(string)
filtered_analyses = []
for analysis in analyses:
if not analysis.is_known:
# Skip suggested parse variant for unknown word for pymorphy
continue
analysis_pos, _ = oc2ud(str(analysis.tag))
if analysis_pos == univ_pos or (
analysis_pos in ("NOUN", "PROPN") and univ_pos in ("NOUN", "PROPN")
):
filtered_analyses.append(analysis)
if not len(filtered_analyses):
return [string.lower()]
if morphology is None or (len(morphology) == 1 and POS in morphology):
return list(set([analysis.normal_form for analysis in filtered_analyses]))
if univ_pos in ("ADJ", "DET", "NOUN", "PROPN"):
features_to_compare = ["Case", "Number", "Gender"]
elif univ_pos == "NUM":
features_to_compare = ["Case", "Gender"]
elif univ_pos == "PRON":
features_to_compare = ["Case", "Number", "Gender", "Person"]
else: # VERB
features_to_compare = [
"Aspect",
"Gender",
"Mood",
"Number",
"Tense",
"VerbForm",
"Voice",
]
analyses, filtered_analyses = filtered_analyses, []
for analysis in analyses:
_, analysis_morph = oc2ud(str(analysis.tag))
for feature in features_to_compare:
if (
feature in morphology
and feature in analysis_morph
and morphology[feature] != analysis_morph[feature]
):
break
else:
filtered_analyses.append(analysis)
if not len(filtered_analyses):
return [string.lower()]
return list(set([analysis.normal_form for analysis in filtered_analyses]))
@staticmethod
def normalize_univ_pos(univ_pos):
if isinstance(univ_pos, str):
return univ_pos.upper()
symbols_to_str = {
ADJ: "ADJ",
DET: "DET",
NOUN: "NOUN",
NUM: "NUM",
PRON: "PRON",
PROPN: "PROPN",
PUNCT: "PUNCT",
VERB: "VERB",
}
if univ_pos in symbols_to_str:
return symbols_to_str[univ_pos]
return None
def is_base_form(self, univ_pos, morphology=None):
# TODO
raise NotImplementedError
def det(self, string, morphology=None):
return self(string, "det", morphology)
def num(self, string, morphology=None):
return self(string, "num", morphology)
def pron(self, string, morphology=None):
return self(string, "pron", morphology)
def lookup(self, string):
analyses = self._morph.parse(string)
if len(analyses) == 1:
return analyses[0].normal_form
return string
def oc2ud(oc_tag):
gram_map = {
"_POS": {
"ADJF": "ADJ",
"ADJS": "ADJ",
"ADVB": "ADV",
"Apro": "DET",
"COMP": "ADJ", # Can also be an ADV - unchangeable
"CONJ": "CCONJ", # Can also be a SCONJ - both unchangeable ones
"GRND": "VERB",
"INFN": "VERB",
"INTJ": "INTJ",
"NOUN": "NOUN",
"NPRO": "PRON",
"NUMR": "NUM",
"NUMB": "NUM",
"PNCT": "PUNCT",
"PRCL": "PART",
"PREP": "ADP",
"PRTF": "VERB",
"PRTS": "VERB",
"VERB": "VERB",
},
"Animacy": {"anim": "Anim", "inan": "Inan"},
"Aspect": {"impf": "Imp", "perf": "Perf"},
"Case": {
"ablt": "Ins",
"accs": "Acc",
"datv": "Dat",
"gen1": "Gen",
"gen2": "Gen",
"gent": "Gen",
"loc2": "Loc",
"loct": "Loc",
"nomn": "Nom",
"voct": "Voc",
},
"Degree": {"COMP": "Cmp", "Supr": "Sup"},
"Gender": {"femn": "Fem", "masc": "Masc", "neut": "Neut"},
"Mood": {"impr": "Imp", "indc": "Ind"},
"Number": {"plur": "Plur", "sing": "Sing"},
"NumForm": {"NUMB": "Digit"},
"Person": {"1per": "1", "2per": "2", "3per": "3", "excl": "2", "incl": "1"},
"Tense": {"futr": "Fut", "past": "Past", "pres": "Pres"},
"Variant": {"ADJS": "Brev", "PRTS": "Brev"},
"VerbForm": {
"GRND": "Conv",
"INFN": "Inf",
"PRTF": "Part",
"PRTS": "Part",
"VERB": "Fin",
},
"Voice": {"actv": "Act", "pssv": "Pass"},
"Abbr": {"Abbr": "Yes"},
}
pos = "X"
morphology = dict()
unmatched = set()
grams = oc_tag.replace(" ", ",").split(",")
for gram in grams:
match = False
for categ, gmap in sorted(gram_map.items()):
if gram in gmap:
match = True
if categ == "_POS":
pos = gmap[gram]
else:
morphology[categ] = gmap[gram]
if not match:
unmatched.add(gram)
while len(unmatched) > 0:
gram = unmatched.pop()
if gram in ("Name", "Patr", "Surn", "Geox", "Orgn"):
pos = "PROPN"
elif gram == "Auxt":
pos = "AUX"
elif gram == "Pltm":
morphology["Number"] = "Ptan"
return pos, morphology
PUNCT_RULES = {"«": '"', "»": '"'}