# -*- coding: utf-8 -*-
from underthesea import chunk
from .model_crf import CRFNERPredictor
[docs]def ner(sentence, format=None, deep=False):
"""
Location and classify named entities in text
Parameters
==========
sentence: {unicode, str}
raw sentence
Returns
=======
tokens: list of tuple with word, pos tag, chunking tag, ner tag tagged sentence
Examples
--------
>>> # -*- coding: utf-8 -*-
>>> from underthesea import ner
>>> sentence = "Ông Putin ca ngợi những thành tựu vĩ đại của Liên Xô"
>>> ner(sentence)
[('Ông', 'Nc', 'B-NP', 'O'),
('Putin', 'Np', 'B-NP', 'B-PER'),
('ca ngợi', 'V', 'B-VP', 'O'),
('những', 'L', 'B-NP', 'O'),
('thành tựu', 'N', 'B-NP', 'O'),
('vĩ đại', 'A', 'B-AP', 'O'),
('của', 'E', 'B-PP', 'O'),
('Liên Xô', 'Np', 'B-NP', 'B-LOC')]
"""
if not deep:
sentence = chunk(sentence)
crf_model = CRFNERPredictor.Instance()
result = crf_model.predict(sentence, format)
return result
else:
from .model_transformers import nlp
output = nlp(sentence)
if len(output) == 0:
return []
entities = [output[0]]
for item in output[1:]:
if item["word"].startswith("##"):
entities[-1]["word"] = entities[-1]["word"] + item["word"][2:]
entities[-1]["end"] = item["end"]
else:
entities.append(item)
return entities