def _create_dependencies(input_pack, tokens, result):
deps = result['predicted_dependencies']
heads = result['predicted_heads']
for i, token in enumerate(tokens):
relation = Dependency(input_pack,
parent=tokens[heads[i] - 1],
child=token)
relation.set_fields(rel_type=deps[i])
input_pack.add_or_get_entry(relation)
def _process(self, input_pack: DataPack):
doc = input_pack.text
end_pos = 0
# sentence parsing
sentences = self.nlp(doc).sentences # type: ignore
# Iterating through stanfordnlp sentence objects
for sentence in sentences:
begin_pos = doc.find(sentence.words[0].text, end_pos)
end_pos = doc.find(sentence.words[-1].text, begin_pos) + len(
sentence.words[-1].text)
sentence_entry = Sentence(input_pack, begin_pos, end_pos)
input_pack.add_or_get_entry(sentence_entry)
tokens: List[Token] = []
if "tokenize" in self.processors:
offset = sentence_entry.span.begin
end_pos_word = 0
# Iterating through stanfordnlp word objects
for word in sentence.words:
begin_pos_word = sentence_entry.text. \
find(word.text, end_pos_word)
end_pos_word = begin_pos_word + len(word.text)
token = Token(input_pack,
begin_pos_word + offset,
end_pos_word + offset
)
if "pos" in self.processors:
token.set_fields(pos=word.pos)
token.set_fields(upos=word.upos)
token.set_fields(xpos=word.xpos)
if "lemma" in self.processors:
token.set_fields(lemma=word.lemma)
tokens.append(token)
input_pack.add_or_get_entry(token)
# For each sentence, get the dependency relations among tokens
if "depparse" in self.processors:
# Iterating through token entries in current sentence
for token, word in zip(tokens, sentence.words):
child = token # current token
parent = tokens[word.governor - 1] # Root token
relation_entry = Dependency(input_pack, parent, child)
relation_entry.set_fields(
rel_type=word.dependency_relation)
input_pack.add_or_get_entry(relation_entry)