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444 lines
17 KiB
Cython
444 lines
17 KiB
Cython
# cython: infer_types
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# coding: utf8
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from __future__ import unicode_literals
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from libc.string cimport memset
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from .attrs cimport POS, IS_SPACE
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from .attrs import LEMMA, intify_attrs
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from .parts_of_speech cimport SPACE
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from .parts_of_speech import IDS as POS_IDS
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from .lexeme cimport Lexeme
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from .errors import Errors
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def _normalize_props(props):
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"""Transform deprecated string keys to correct names."""
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out = {}
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for key, value in props.items():
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if key == POS:
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if hasattr(value, 'upper'):
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value = value.upper()
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if value in POS_IDS:
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value = POS_IDS[value]
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out[key] = value
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elif isinstance(key, int):
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out[key] = value
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elif key.lower() == 'pos':
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out[POS] = POS_IDS[value.upper()]
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else:
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out[key] = value
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return out
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cdef class Morphology:
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def __init__(self, StringStore string_store, tag_map, lemmatizer, exc=None):
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self.mem = Pool()
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self.strings = string_store
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# Add special space symbol. We prefix with underscore, to make sure it
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# always sorts to the end.
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space_attrs = tag_map.get('SP', {POS: SPACE})
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if '_SP' not in tag_map:
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self.strings.add('_SP')
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tag_map = dict(tag_map)
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tag_map['_SP'] = space_attrs
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self.tag_names = tuple(sorted(tag_map.keys()))
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self.tag_map = {}
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self.lemmatizer = lemmatizer
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self.n_tags = len(tag_map)
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self.reverse_index = {}
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self.rich_tags = <RichTagC*>self.mem.alloc(self.n_tags+1, sizeof(RichTagC))
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for i, (tag_str, attrs) in enumerate(sorted(tag_map.items())):
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self.strings.add(tag_str)
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self.tag_map[tag_str] = dict(attrs)
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attrs = _normalize_props(attrs)
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attrs = intify_attrs(attrs, self.strings, _do_deprecated=True)
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self.rich_tags[i].id = i
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self.rich_tags[i].name = self.strings.add(tag_str)
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self.rich_tags[i].morph = 0
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self.rich_tags[i].pos = attrs[POS]
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self.reverse_index[self.rich_tags[i].name] = i
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# Add a 'null' tag, which we can reference when assign morphology to
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# untagged tokens.
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self.rich_tags[self.n_tags].id = self.n_tags
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self._cache = PreshMapArray(self.n_tags)
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self.exc = {}
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if exc is not None:
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for (tag_str, orth_str), attrs in exc.items():
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self.add_special_case(tag_str, orth_str, attrs)
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def __reduce__(self):
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return (Morphology, (self.strings, self.tag_map, self.lemmatizer,
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self.exc), None, None)
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cdef int assign_untagged(self, TokenC* token) except -1:
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"""Set morphological attributes on a token without a POS tag. Uses
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the lemmatizer's lookup() method, which looks up the string in the
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table provided by the language data as lemma_lookup (if available).
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"""
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if token.lemma == 0:
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orth_str = self.strings[token.lex.orth]
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lemma = self.lemmatizer.lookup(orth_str)
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token.lemma = self.strings.add(lemma)
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cdef int assign_tag(self, TokenC* token, tag) except -1:
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if isinstance(tag, basestring):
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tag = self.strings.add(tag)
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if tag in self.reverse_index:
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tag_id = self.reverse_index[tag]
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self.assign_tag_id(token, tag_id)
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else:
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token.tag = tag
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cdef int assign_tag_id(self, TokenC* token, int tag_id) except -1:
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if tag_id > self.n_tags:
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raise ValueError(Errors.E014.format(tag=tag_id))
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# TODO: It's pretty arbitrary to put this logic here. I guess the
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# justification is that this is where the specific word and the tag
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# interact. Still, we should have a better way to enforce this rule, or
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# figure out why the statistical model fails. Related to Issue #220
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if Lexeme.c_check_flag(token.lex, IS_SPACE):
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tag_id = self.reverse_index[self.strings.add('_SP')]
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rich_tag = self.rich_tags[tag_id]
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analysis = <MorphAnalysisC*>self._cache.get(tag_id, token.lex.orth)
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if analysis is NULL:
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analysis = <MorphAnalysisC*>self.mem.alloc(1, sizeof(MorphAnalysisC))
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tag_str = self.strings[self.rich_tags[tag_id].name]
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analysis.tag = rich_tag
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analysis.lemma = self.lemmatize(analysis.tag.pos, token.lex.orth,
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self.tag_map.get(tag_str, {}))
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self._cache.set(tag_id, token.lex.orth, analysis)
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token.lemma = analysis.lemma
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token.pos = analysis.tag.pos
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token.tag = analysis.tag.name
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token.morph = analysis.tag.morph
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cdef int assign_feature(self, uint64_t* flags, univ_morph_t flag_id, bint value) except -1:
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cdef flags_t one = 1
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if value:
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flags[0] |= one << flag_id
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else:
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flags[0] &= ~(one << flag_id)
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def add_special_case(self, unicode tag_str, unicode orth_str, attrs,
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force=False):
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"""Add a special-case rule to the morphological analyser. Tokens whose
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tag and orth match the rule will receive the specified properties.
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tag (unicode): The part-of-speech tag to key the exception.
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orth (unicode): The word-form to key the exception.
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"""
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# TODO: Currently we've assumed that we know the number of tags --
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# RichTagC is an array, and _cache is a PreshMapArray
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# This is really bad: it makes the morphology typed to the tagger
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# classes, which is all wrong.
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self.exc[(tag_str, orth_str)] = dict(attrs)
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tag = self.strings.add(tag_str)
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if tag not in self.reverse_index:
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return
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tag_id = self.reverse_index[tag]
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orth = self.strings[orth_str]
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cdef RichTagC rich_tag = self.rich_tags[tag_id]
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attrs = intify_attrs(attrs, self.strings, _do_deprecated=True)
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cached = <MorphAnalysisC*>self._cache.get(tag_id, orth)
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if cached is NULL:
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cached = <MorphAnalysisC*>self.mem.alloc(1, sizeof(MorphAnalysisC))
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elif force:
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memset(cached, 0, sizeof(cached[0]))
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else:
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raise ValueError(Errors.E015.format(tag=tag_str, orth=orth_str))
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cached.tag = rich_tag
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# TODO: Refactor this to take arbitrary attributes.
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for name_id, value_id in attrs.items():
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if name_id == LEMMA:
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cached.lemma = value_id
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else:
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self.assign_feature(&cached.tag.morph, name_id, value_id)
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if cached.lemma == 0:
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cached.lemma = self.lemmatize(rich_tag.pos, orth, attrs)
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self._cache.set(tag_id, orth, <void*>cached)
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def load_morph_exceptions(self, dict exc):
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# Map (form, pos) to (lemma, rich tag)
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for tag_str, entries in exc.items():
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for form_str, attrs in entries.items():
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self.add_special_case(tag_str, form_str, attrs)
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def lemmatize(self, const univ_pos_t univ_pos, attr_t orth, morphology):
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if orth not in self.strings:
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return orth
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cdef unicode py_string = self.strings[orth]
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if self.lemmatizer is None:
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return self.strings.add(py_string.lower())
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cdef list lemma_strings
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cdef unicode lemma_string
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lemma_strings = self.lemmatizer(py_string, univ_pos, morphology)
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lemma_string = lemma_strings[0]
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lemma = self.strings.add(lemma_string)
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return lemma
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IDS = {
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"Animacy_anim": Animacy_anim,
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"Animacy_inan": Animacy_inan,
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"Animacy_hum": Animacy_hum, # U20
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"Animacy_nhum": Animacy_nhum,
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"Aspect_freq": Aspect_freq,
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"Aspect_imp": Aspect_imp,
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"Aspect_mod": Aspect_mod,
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"Aspect_none": Aspect_none,
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"Aspect_perf": Aspect_perf,
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"Case_abe": Case_abe,
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"Case_abl": Case_abl,
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"Case_abs": Case_abs,
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"Case_acc": Case_acc,
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"Case_ade": Case_ade,
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"Case_all": Case_all,
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"Case_cau": Case_cau,
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"Case_com": Case_com,
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"Case_dat": Case_dat,
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"Case_del": Case_del,
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"Case_dis": Case_dis,
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"Case_ela": Case_ela,
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"Case_ess": Case_ess,
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"Case_gen": Case_gen,
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"Case_ill": Case_ill,
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"Case_ine": Case_ine,
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"Case_ins": Case_ins,
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"Case_loc": Case_loc,
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"Case_lat": Case_lat,
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"Case_nom": Case_nom,
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"Case_par": Case_par,
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"Case_sub": Case_sub,
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"Case_sup": Case_sup,
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"Case_tem": Case_tem,
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"Case_ter": Case_ter,
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"Case_tra": Case_tra,
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"Case_voc": Case_voc,
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"Definite_two": Definite_two,
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"Definite_def": Definite_def,
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"Definite_red": Definite_red,
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"Definite_cons": Definite_cons, # U20
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"Definite_ind": Definite_ind,
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"Degree_cmp": Degree_cmp,
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"Degree_comp": Degree_comp,
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"Degree_none": Degree_none,
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"Degree_pos": Degree_pos,
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"Degree_sup": Degree_sup,
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"Degree_abs": Degree_abs,
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"Degree_com": Degree_com,
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"Degree_dim ": Degree_dim, # du
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"Gender_com": Gender_com,
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"Gender_fem": Gender_fem,
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"Gender_masc": Gender_masc,
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"Gender_neut": Gender_neut,
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"Mood_cnd": Mood_cnd,
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"Mood_imp": Mood_imp,
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"Mood_ind": Mood_ind,
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"Mood_n": Mood_n,
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"Mood_pot": Mood_pot,
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"Mood_sub": Mood_sub,
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"Mood_opt": Mood_opt,
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"Negative_neg": Negative_neg,
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"Negative_pos": Negative_pos,
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"Negative_yes": Negative_yes,
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"Polarity_neg": Polarity_neg, # U20
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"Polarity_pos": Polarity_pos, # U20
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"Number_com": Number_com,
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"Number_dual": Number_dual,
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"Number_none": Number_none,
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"Number_plur": Number_plur,
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"Number_sing": Number_sing,
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"Number_ptan ": Number_ptan, # bg
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"Number_count ": Number_count, # bg
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"NumType_card": NumType_card,
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"NumType_dist": NumType_dist,
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"NumType_frac": NumType_frac,
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"NumType_gen": NumType_gen,
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"NumType_mult": NumType_mult,
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"NumType_none": NumType_none,
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"NumType_ord": NumType_ord,
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"NumType_sets": NumType_sets,
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"Person_one": Person_one,
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"Person_two": Person_two,
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"Person_three": Person_three,
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"Person_none": Person_none,
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"Poss_yes": Poss_yes,
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"PronType_advPart": PronType_advPart,
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"PronType_art": PronType_art,
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"PronType_default": PronType_default,
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"PronType_dem": PronType_dem,
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"PronType_ind": PronType_ind,
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"PronType_int": PronType_int,
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"PronType_neg": PronType_neg,
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"PronType_prs": PronType_prs,
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"PronType_rcp": PronType_rcp,
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"PronType_rel": PronType_rel,
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"PronType_tot": PronType_tot,
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"PronType_clit": PronType_clit,
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"PronType_exc ": PronType_exc, # es, ca, it, fa,
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"Reflex_yes": Reflex_yes,
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"Tense_fut": Tense_fut,
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"Tense_imp": Tense_imp,
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"Tense_past": Tense_past,
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"Tense_pres": Tense_pres,
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"VerbForm_fin": VerbForm_fin,
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"VerbForm_ger": VerbForm_ger,
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"VerbForm_inf": VerbForm_inf,
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"VerbForm_none": VerbForm_none,
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"VerbForm_part": VerbForm_part,
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"VerbForm_partFut": VerbForm_partFut,
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"VerbForm_partPast": VerbForm_partPast,
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"VerbForm_partPres": VerbForm_partPres,
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"VerbForm_sup": VerbForm_sup,
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"VerbForm_trans": VerbForm_trans,
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"VerbForm_conv": VerbForm_conv, # U20
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"VerbForm_gdv ": VerbForm_gdv, # la,
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"Voice_act": Voice_act,
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"Voice_cau": Voice_cau,
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"Voice_pass": Voice_pass,
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"Voice_mid ": Voice_mid, # gkc,
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"Voice_int ": Voice_int, # hb,
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"Abbr_yes ": Abbr_yes, # cz, fi, sl, U,
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"AdpType_prep ": AdpType_prep, # cz, U,
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"AdpType_post ": AdpType_post, # U,
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"AdpType_voc ": AdpType_voc, # cz,
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"AdpType_comprep ": AdpType_comprep, # cz,
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"AdpType_circ ": AdpType_circ, # U,
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"AdvType_man": AdvType_man,
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"AdvType_loc": AdvType_loc,
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"AdvType_tim": AdvType_tim,
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"AdvType_deg": AdvType_deg,
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"AdvType_cau": AdvType_cau,
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"AdvType_mod": AdvType_mod,
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"AdvType_sta": AdvType_sta,
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"AdvType_ex": AdvType_ex,
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"AdvType_adadj": AdvType_adadj,
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"ConjType_oper ": ConjType_oper, # cz, U,
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"ConjType_comp ": ConjType_comp, # cz, U,
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"Connegative_yes ": Connegative_yes, # fi,
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"Derivation_minen ": Derivation_minen, # fi,
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"Derivation_sti ": Derivation_sti, # fi,
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"Derivation_inen ": Derivation_inen, # fi,
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"Derivation_lainen ": Derivation_lainen, # fi,
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"Derivation_ja ": Derivation_ja, # fi,
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"Derivation_ton ": Derivation_ton, # fi,
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"Derivation_vs ": Derivation_vs, # fi,
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"Derivation_ttain ": Derivation_ttain, # fi,
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"Derivation_ttaa ": Derivation_ttaa, # fi,
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"Echo_rdp ": Echo_rdp, # U,
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"Echo_ech ": Echo_ech, # U,
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"Foreign_foreign ": Foreign_foreign, # cz, fi, U,
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"Foreign_fscript ": Foreign_fscript, # cz, fi, U,
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"Foreign_tscript ": Foreign_tscript, # cz, U,
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"Foreign_yes ": Foreign_yes, # sl,
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"Gender_dat_masc ": Gender_dat_masc, # bq, U,
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"Gender_dat_fem ": Gender_dat_fem, # bq, U,
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"Gender_erg_masc ": Gender_erg_masc, # bq,
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"Gender_erg_fem ": Gender_erg_fem, # bq,
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"Gender_psor_masc ": Gender_psor_masc, # cz, sl, U,
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"Gender_psor_fem ": Gender_psor_fem, # cz, sl, U,
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"Gender_psor_neut ": Gender_psor_neut, # sl,
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"Hyph_yes ": Hyph_yes, # cz, U,
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"InfForm_one ": InfForm_one, # fi,
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"InfForm_two ": InfForm_two, # fi,
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"InfForm_three ": InfForm_three, # fi,
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"NameType_geo ": NameType_geo, # U, cz,
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"NameType_prs ": NameType_prs, # U, cz,
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"NameType_giv ": NameType_giv, # U, cz,
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"NameType_sur ": NameType_sur, # U, cz,
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"NameType_nat ": NameType_nat, # U, cz,
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"NameType_com ": NameType_com, # U, cz,
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"NameType_pro ": NameType_pro, # U, cz,
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"NameType_oth ": NameType_oth, # U, cz,
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"NounType_com ": NounType_com, # U,
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"NounType_prop ": NounType_prop, # U,
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"NounType_class ": NounType_class, # U,
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"Number_abs_sing ": Number_abs_sing, # bq, U,
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"Number_abs_plur ": Number_abs_plur, # bq, U,
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"Number_dat_sing ": Number_dat_sing, # bq, U,
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"Number_dat_plur ": Number_dat_plur, # bq, U,
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"Number_erg_sing ": Number_erg_sing, # bq, U,
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"Number_erg_plur ": Number_erg_plur, # bq, U,
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"Number_psee_sing ": Number_psee_sing, # U,
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"Number_psee_plur ": Number_psee_plur, # U,
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"Number_psor_sing ": Number_psor_sing, # cz, fi, sl, U,
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"Number_psor_plur ": Number_psor_plur, # cz, fi, sl, U,
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"NumForm_digit ": NumForm_digit, # cz, sl, U,
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"NumForm_roman ": NumForm_roman, # cz, sl, U,
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"NumForm_word ": NumForm_word, # cz, sl, U,
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"NumValue_one ": NumValue_one, # cz, U,
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"NumValue_two ": NumValue_two, # cz, U,
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"NumValue_three ": NumValue_three, # cz, U,
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"PartForm_pres ": PartForm_pres, # fi,
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"PartForm_past ": PartForm_past, # fi,
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"PartForm_agt ": PartForm_agt, # fi,
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"PartForm_neg ": PartForm_neg, # fi,
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"PartType_mod ": PartType_mod, # U,
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"PartType_emp ": PartType_emp, # U,
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"PartType_res ": PartType_res, # U,
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"PartType_inf ": PartType_inf, # U,
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"PartType_vbp ": PartType_vbp, # U,
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"Person_abs_one ": Person_abs_one, # bq, U,
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"Person_abs_two ": Person_abs_two, # bq, U,
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"Person_abs_three ": Person_abs_three, # bq, U,
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"Person_dat_one ": Person_dat_one, # bq, U,
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"Person_dat_two ": Person_dat_two, # bq, U,
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"Person_dat_three ": Person_dat_three, # bq, U,
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"Person_erg_one ": Person_erg_one, # bq, U,
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"Person_erg_two ": Person_erg_two, # bq, U,
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"Person_erg_three ": Person_erg_three, # bq, U,
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"Person_psor_one ": Person_psor_one, # fi, U,
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"Person_psor_two ": Person_psor_two, # fi, U,
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"Person_psor_three ": Person_psor_three, # fi, U,
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"Polite_inf ": Polite_inf, # bq, U,
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"Polite_pol ": Polite_pol, # bq, U,
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"Polite_abs_inf ": Polite_abs_inf, # bq, U,
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"Polite_abs_pol ": Polite_abs_pol, # bq, U,
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"Polite_erg_inf ": Polite_erg_inf, # bq, U,
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"Polite_erg_pol ": Polite_erg_pol, # bq, U,
|
|
"Polite_dat_inf ": Polite_dat_inf, # bq, U,
|
|
"Polite_dat_pol ": Polite_dat_pol, # bq, U,
|
|
"Prefix_yes ": Prefix_yes, # U,
|
|
"PrepCase_npr ": PrepCase_npr, # cz,
|
|
"PrepCase_pre ": PrepCase_pre, # U,
|
|
"PunctSide_ini ": PunctSide_ini, # U,
|
|
"PunctSide_fin ": PunctSide_fin, # U,
|
|
"PunctType_peri ": PunctType_peri, # U,
|
|
"PunctType_qest ": PunctType_qest, # U,
|
|
"PunctType_excl ": PunctType_excl, # U,
|
|
"PunctType_quot ": PunctType_quot, # U,
|
|
"PunctType_brck ": PunctType_brck, # U,
|
|
"PunctType_comm ": PunctType_comm, # U,
|
|
"PunctType_colo ": PunctType_colo, # U,
|
|
"PunctType_semi ": PunctType_semi, # U,
|
|
"PunctType_dash ": PunctType_dash, # U,
|
|
"Style_arch ": Style_arch, # cz, fi, U,
|
|
"Style_rare ": Style_rare, # cz, fi, U,
|
|
"Style_poet ": Style_poet, # cz, U,
|
|
"Style_norm ": Style_norm, # cz, U,
|
|
"Style_coll ": Style_coll, # cz, U,
|
|
"Style_vrnc ": Style_vrnc, # cz, U,
|
|
"Style_sing ": Style_sing, # cz, U,
|
|
"Style_expr ": Style_expr, # cz, U,
|
|
"Style_derg ": Style_derg, # cz, U,
|
|
"Style_vulg ": Style_vulg, # cz, U,
|
|
"Style_yes ": Style_yes, # fi, U,
|
|
"StyleVariant_styleShort ": StyleVariant_styleShort, # cz,
|
|
"StyleVariant_styleBound ": StyleVariant_styleBound, # cz, sl,
|
|
"VerbType_aux ": VerbType_aux, # U,
|
|
"VerbType_cop ": VerbType_cop, # U,
|
|
"VerbType_mod ": VerbType_mod, # U,
|
|
"VerbType_light ": VerbType_light, # U,
|
|
}
|
|
|
|
|
|
NAMES = [key for key, value in sorted(IDS.items(), key=lambda item: item[1])]
|
|
# Unfortunate hack here, to work around problem with long cpdef enum
|
|
# (which is generating an enormous amount of C++ in Cython 0.24+)
|
|
# We keep the enum cdef, and just make sure the names are available to Python
|
|
locals().update(IDS)
|