def prettify(self, tokens: List[str], tags: List[str],
lemmas: List[str]) -> Union[List[str], str]:
"""Prettifies output of morphological tagger.
Args:
tokens: tokenized source sentence
tags: list of tags, the output of a tagger
lemmas: list of lemmas, the output of a lemmatizer
Returns:
the prettified output of the tagger.
Examples:
>>> sent = "John really likes pizza .".split()
>>> tags = ["PROPN,Number=Sing", "ADV",
>>> "VERB,Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin",
>>> "NOUN,Number=Sing", "PUNCT"]
>>> lemmas = "John really like pizza .".split()
>>> prettifier = LemmatizedOutputPrettifier()
>>> self.prettify(sent, tags, lemmas)
1 John John PROPN _ Number=Sing _ _ _ _
2 really really ADV _ _ _ _ _ _
3 likes like VERB _ Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin _ _ _ _
4 pizza pizza NOUN _ Number=Sing _ _ _ _
5 . . PUNCT _ _ _ _ _ _
"""
answer = []
for i, (word, tag, lemma) in enumerate(zip(tokens, tags, lemmas)):
pos, tag = make_pos_and_tag(tag, sep=",")
answer.append(
self.format_string.format(i + 1, word, pos, tag, lemma))
if self.return_string:
answer = self.begin + self.sep.join(answer) + self.end
return answer
def prettify(sent, tags, return_string=True, begin="", end="", sep="\n"):
"""
x: str, sentence
y: list, a sequence of tags
x = "John likes, really likes pizza"
y = ["NNP", "VBZ", "PUNCT", "RB", "VBZ", "NN"]
answer:
1 John NNP
2 likes VBZ
3 , PUNCT
4 really RB
5 likes VBZ
6 pizza NN
7 . SENT
"""
if isinstance(sent, str):
words = [x for x in re.split("(\w+|[,.])", sent) if x.strip() != ""]
else:
words = sent
answer = []
for i, (word, tag) in enumerate(zip(words, tags)):
answer.append("{}\t{}\t{}\t{}".format(i+1, word, *make_pos_and_tag(tag)))
if return_string:
answer = begin + sep.join(answer) + end
return answer
def prettify(sent: Union[str, List[str]],
tags: List[str],
return_string: bool = True,
begin: str = "",
end: str = "",
sep: str = "\n") -> Union[List[str], str]:
"""Prettifies output of morphological tagger.
Args:
sent: source sentence (either tokenized or not)
tags: list of tags, the output of a tagger
return_string: whether to return a list of strings or a single string
begin: a string to append in the beginning
end: a string to append in the end
sep: separator between word analyses
Returns:
the prettified output of the tagger.
Examples:
>>> sent = "John likes, really likes pizza"
>>> tags = ["NNP", "VBZ", "PUNCT", "RB", "VBZ", "NN"]
>>> prettify(sent, tags)
1 John NNP
2 likes VBZ
3 , PUNCT
4 really RB
5 likes VBZ
6 pizza NN
7 . SENT
"""
if isinstance(sent, str):
words = [x for x in re.split("(\w+|[,.])", sent) if x.strip() != ""]
else:
words = sent
answer = []
for i, (word, tag) in enumerate(zip(words, tags)):
answer.append("{}\t{}\t{}\t{}".format(i + 1, word,
*make_pos_and_tag(tag)))
if return_string:
answer = begin + sep.join(answer) + end
return answer
def _make_tag_trie(self):
self._nodes = [defaultdict(dict)]
self._start_nodes_for_pos = dict()
self._data = [None]
for tag, code in self._t2i.items():
pos, tag = make_pos_and_tag(tag, sep=",", return_mode="sorted_items")
start = self._start_nodes_for_pos.get(pos)
if start is None:
start = self._start_nodes_for_pos[pos] = len(self._nodes)
self._nodes.append(defaultdict(dict))
self._data.append(None)
for key, value in tag:
values_dict = self._nodes[start][key]
child = values_dict.get(value)
if child is None:
child = values_dict[value] = len(self._nodes)
self._nodes.append(defaultdict(dict))
self._data.append(None)
start = child
self._data[start] = code
return self
def prettify(self, tokens: List[str],
tags: List[str]) -> Union[List[str], str]:
"""Prettifies output of morphological tagger.
Args:
tokens: tokenized source sentence
tags: list of tags, the output of a tagger
Returns:
the prettified output of the tagger.
Examples:
>>> sent = "John really likes pizza .".split()
>>> tags = ["PROPN,Number=Sing", "ADV",
>>> "VERB,Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin",
>>> "NOUN,Number=Sing", "PUNCT"]
>>> prettifier = TagOutputPrettifier(mode='basic')
>>> self.prettify(sent, tags)
1 John PROPN Number=Sing
2 really ADV _
3 likes VERB Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin
4 pizza NOUN Number=Sing
5 . PUNCT _
>>> prettifier = TagOutputPrettifier(mode='ud')
>>> self.prettify(sent, tags)
1 John _ PROPN _ Number=Sing _ _ _ _
2 really _ ADV _ _ _ _ _ _
3 likes _ VERB _ Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin _ _ _ _
4 pizza _ NOUN _ Number=Sing _ _ _ _
5 . _ PUNCT _ _ _ _ _ _
"""
answer = []
for i, (word, tag) in enumerate(zip(tokens, tags)):
answer.append(
self.format_string.format(i + 1, word, *make_pos_and_tag(tag)))
if self.return_string:
answer = self.begin + self.sep.join(answer) + self.end
return answer
def prettify(self, tokens: List[str], tags: List[str]) -> Union[List[str], str]:
"""Prettifies output of morphological tagger.
Args:
tokens: tokenized source sentence
tags: list of tags, the output of a tagger
Returns:
the prettified output of the tagger.
Examples:
>>> sent = "John really likes pizza .".split()
>>> tags = ["PROPN,Number=Sing", "ADV",
>>> "VERB,Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin",
>>> "NOUN,Number=Sing", "PUNCT"]
>>> prettifier = TagOutputPrettifier(mode='basic')
>>> self.prettify(sent, tags)
1 John PROPN Number=Sing
2 really ADV _
3 likes VERB Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin
4 pizza NOUN Number=Sing
5 . PUNCT _
>>> prettifier = TagOutputPrettifier(mode='ud')
>>> self.prettify(sent, tags)
1 John _ PROPN _ Number=Sing _ _ _ _
2 really _ ADV _ _ _ _ _ _
3 likes _ VERB _ Mood=Ind|Number=Sing|Person=3|Tense=Pres|VerbForm=Fin _ _ _ _
4 pizza _ NOUN _ Number=Sing _ _ _ _
5 . _ PUNCT _ _ _ _ _ _
"""
answer = []
for i, (word, tag) in enumerate(zip(tokens, tags)):
answer.append(self.format_string.format(i + 1, word, *make_pos_and_tag(tag)))
if self.return_string:
answer = self.begin + self.sep.join(answer) + self.end
return answer
