def __init__(
self, cutoff: DiscreteValue(min=0, max=10),
):
self.cutoff = cutoff
self.tagger = _UnigramTagger
self.values = dict(cutoff=cutoff)
NltkTagger.__init__(self)
def __init__(
self,
affix_length: Discrete(min=-2, max=-6),
min_stem_length: Discrete(min=1, max=4),
cutoff: Discrete(min=0, max=10),
backoff: algorithm(Tuple(List(List(Word())), List(List(Postag()))),
List(List(Postag()))),
):
self.affix_length = affix_length
self.min_stem_length = min_stem_length
self.cutoff = cutoff
self.backoff = backoff
self.tagger = _AffixTagger
self.values = dict(
affix_length=affix_length,
min_stem_length=min_stem_length,
cutoff=cutoff,
backoff=backoff,
)
NltkTagger.__init__(self)
def __init__(
self,
affix_length: DiscreteValue(min=2, max=6),
min_stem_length: DiscreteValue(min=1, max=4),
cutoff: DiscreteValue(min=0, max=10),
backoff: algorithm(
Seq[Seq[Word]], Supervised[Seq[Seq[Postag]]], Seq[Seq[Postag]]
),
):
self.affix_length = affix_length
self.min_stem_length = min_stem_length
self.cutoff = cutoff
self.backoff = backoff
self.tagger = _AffixTagger
self.values = dict(
affix_length=affix_length,
min_stem_length=min_stem_length,
cutoff=cutoff,
backoff=backoff,
)
NltkTagger.__init__(self)
def run(self, input: Seq[Postag]) -> Seq[Chunktag]:
return NltkTagger.run(self, input)
def __init__(self, ):
self.tagger = _NEChunkParserTagger
self.values = dict()
NltkTagger.__init__(self)
def run(self, input: List(List(Postag()))) -> List(List(Chunktag())):
return NltkTagger.run(self, input)
def run(
self, input: Seq[Seq[Word]], y: Supervised[Seq[Seq[Postag]]]
) -> Seq[Seq[Postag]]:
return NltkTagger.run(self, input, y)
def __init__(self,):
self.tagger = _ClassifierBasedPOSTagger
self.values = dict()
NltkTagger.__init__(self)
def run(
self, input: Tuple(List(List(Word())), List(List(Postag())))
) -> List(List(Postag())):
return NltkTagger.run(self, input)