def __normalize_document(self, document: str) -> List[str]:
tokens = []
tokenizer = self.__model.newTokenizer(self.__model.DEFAULT)
tokenizer.setText(document)
error = udpipe.ProcessingError()
sentence = udpipe.Sentence()
while tokenizer.nextSentence(sentence, error):
self.__model.tag(sentence, self.__model.DEFAULT)
# 1: is used because words[0] is the root required by the dependency trees
tokens.extend([w.lemma for w in sentence.words[1:]])
sentence = udpipe.Sentence()
return tokens
def __normalize_document(self, document: str) -> List[str]:
tokens = []
tokenizer = self.__model.newTokenizer(self.__model.DEFAULT)
tokenizer.setText(document)
error = udpipe.ProcessingError()
sentence = udpipe.Sentence()
while tokenizer.nextSentence(sentence, error):
self.__model.tag(sentence, self.__model.DEFAULT)
output = self.__output_format.writeSentence(sentence)
sentence = udpipe.Sentence()
tokens.extend([t['properties']['lemma']
for t in json.loads(output)['nodes']])
return tokens
def _read(self, text, input_format):
input_format.setText(text)
error = udpipe.ProcessingError()
sentences = []
sentence = udpipe.Sentence()
while input_format.nextSentence(sentence, error):
sentences.append(sentence)
sentence = udpipe.Sentence()
if error.occurred():
raise Exception(error.message)
return sentences
def normalize(self, token):
self.load_model()
sentence = udpipe.Sentence()
sentence.addWord(token)
self.model.tag(sentence, self.model.DEFAULT)
output = self.output_format.writeSentence(sentence)
return json.loads(output)['nodes'][0]['properties']['lemma']
def segment(self, text):
sent = ud.Sentence()
tokenizer = self.model.newTokenizer('ranges')
tokenizer.setText(text)
sentences = []
tokens = []
raw_tokens = []
sent_start = 0
while tokenizer.nextSentence(sent):
words = sent.words[1:]
sent_raw_tokens = [(word.getTokenRangeStart(),
word.getTokenRangeEnd()) for word in words]
sentences.append(
Sentence(sent_start, sent_start + len(sent_raw_tokens)))
tokens += [
text[raw_token[0]:raw_token[1]]
for raw_token in sent_raw_tokens
]
raw_tokens += sent_raw_tokens
sent_start += len(sent_raw_tokens)
return tokens, sentences, raw_tokens
def convert_to_ud(sentence: NavigableSentence) -> ud.Sentence:
result = ud.Sentence()
for token in sentence:
word = result.addWord(token.text) # type: ud.Word
if token.lemma:
word.lemma = token.lemma
if token.pos:
if token.pos.upos:
word.upostag = token.pos.upos
if token.pos.xpos:
word.xpostag = token.pos.xpos
if token.pos.feats:
word.feats = "|".join(
sorted("{}={}".format(*item)
for item in token.pos.feats.items()))
return result
def __normalize_token(self, token: str) -> str:
sentence = udpipe.Sentence()
sentence.addWord(token)
self.__model.tag(sentence, self.__model.DEFAULT)
output = self.__output_format.writeSentence(sentence)
return json.loads(output)['nodes'][0]['properties']['lemma']
def udpipe_lemmatizer(token):
sentence = udpipe.Sentence()
sentence.addWord(token)
model.tag(sentence, model.DEFAULT)
output = output_format.writeSentence(sentence)
return json.loads(output)['nodes'][0]['properties']['lemma']
def __normalize_token(self, token: str) -> str:
sentence = udpipe.Sentence()
sentence.addWord(token)
self.__model.tag(sentence, self.__model.DEFAULT)
return sentence.words[1].lemma
