class UDPipeTokenizer:
def __init__(self, udpipe_model_path):
self.udpipe_model = Model(udpipe_model_path)
def tokenize(self, sentence: str) -> List[Tuple[str, str]]:
"""
return: list of pairs of tags (POS, DEP_REL) for each token in the sentence
"""
s = list(self.udpipe_model.process(sentence))
lst = [(item.upostag, item.deprel) for item in s[0].words
if item.upostag != '<root>']
return lst
class UdpipeTagger:
def __init__(self, file = None, **kwarg):
if file:
self.model = Model(file)
else:
raise Exception("You should pass the model")
def get_pos_tag(self, word):
sent = list(self.model.process(word))[0]
if len(sent.words) != 2:
print(word, sent.words)
return sent.words[1].xpostag
def do_train(self) -> List[TResult]:
"""
By pre-train modules of unpipe get the results for our corpus
These udpipe modules can be download here:
https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3131
:return:
"""
model = Model(self.pre_model_name)
# train our corpus to get POS for each word
line_no = 1
for sen in self.load_data():
sen_clean = self.clean_data(sen)
if not sen_clean:
continue
word_pos = list(model.process(sen_clean))
for i, one_sentence in enumerate(word_pos):
sentence_text = self.extract_one_sentence(one_sentence)
results = self.extract_one_word(one_sentence, sentence_text)
self.store_data.insert_data(self.cursor, results, self.language_name)
print('line %d, batch %d for %s written succeed' % (line_no, i, self.language_name))
line_no += 1
print(' all written succeed for corpus of %s' % self.our_corpus_name)
def corpy_udpipe(text,
sent_level=True,
model='english-lines-ud-2.5-191206.udpipe'):
m = Model('../udpipe_model/' + model)
print(model, "loaded successfully!")
if sent_level:
all_pos = []
all_head = []
all_dep = []
all_tok = []
for line in text:
#print(line)
sent_pos = []
sent_head = []
sent_dep = []
sent_tok = []
sents = list(m.process(line, out_format="conllu"))
conllu = "".join(sents)
parse_con = parse(conllu)
# iterate over each word and append the POS/HEAD/UD into a list,
#print(parse_con[0])
for i in range(len(parse_con)):
for word in parse_con[i]:
#print(i)
sent_pos.append(word['upostag'])
sent_head.append(word['head'])
sent_dep.append(word['deprel'])
sent_tok.append(word['form'])
# append sent pos to the the doc
all_pos.append(sent_pos)
all_head.append(sent_head)
all_dep.append(sent_dep)
all_tok.append(sent_tok)
# for doc-level
else:
all_pos = []
all_head = []
all_dep = []
all_tok = []
for doc in text:
pos_per_doc = []
head_per_doc = []
dep_per_doc = []
tok_per_doc = []
for line in doc:
#print(line)
sent_pos = []
sent_head = []
sent_dep = []
sent_tok = []
sents = list(m.process(line, out_format="conllu"))
conllu = "".join(sents)
parse_con = parse(conllu)
# iterate over each word and append the POS/HEAD/UD into a list,
#print(parse_con[0])
for i in range(len(parse_con)):
for word in parse_con[i]:
#print(i)
sent_pos.append(word['upostag'])
sent_head.append(word['head'])
sent_dep.append(word['deprel'])
sent_tok.append(word['form'])
# append sent pos to the the doc
pos_per_doc.append(sent_pos)
head_per_doc.append(sent_head)
dep_per_doc.append(sent_dep)
tok_per_doc.append(sent_tok)
all_pos.append(pos_per_doc)
all_head.append(head_per_doc)
all_dep.append(dep_per_doc)
all_tok.append(tok_per_doc)
return all_pos, all_head, all_dep, all_tok
class UdpipeTrain(ITrain):
def __init__(self, language_name, pre_model_name, our_corpus_name):
"""
The language of pre_model_name and our_corpus_name should be identical!
:param language_name:
:param pre_model_name: it's from udpipe
:param our_corpus_name: it's our found
"""
self.language_name = language_name
self.pre_model_name = pre_model_name
self.our_corpus_name = our_corpus_name
try:
self.store_data = StoreData(db_config['user'],
db_config['password'],
db_host=db_config['db_host'],
db_name=db_config['db_name'])
self.cursor = self.store_data.db_connect().cursor()
# second loading udpipe pre-train model
self.model = Model(self.pre_model_name)
except Exception as ex:
print('logging in database error %s' % ex)
def load_data(self) -> str:
with open(self.our_corpus_name, 'r') as f:
for sen in f:
print('loading one sentence: %s' % (sen,))
yield sen
print('loading done for our corpus')
def clean_data(self, data: str) -> str:
"""
data is one or several sentence(s) we expect
if data is \n, \t, empty str, etc, replace them
:param data: raw data
:return: data after cleaning
"""
cleaned_data = re.sub('[\n\t]+', '', data)
return cleaned_data
def do_train(self) -> List[TResult]:
"""
By pre-train modules of unpipe get the results for our corpus
These udpipe modules can be download here:
https://lindat.mff.cuni.cz/repository/xmlui/handle/11234/1-3131
:return:
"""
# train our corpus to get POS for each word
line_no = 1
for sen in self.load_data():
# if line_no < 1811:
# line_no += 1
# continue
sen_clean = self.clean_data(sen)
if not sen_clean:
continue
word_pos = list(self.model.process(sen_clean))
# pprint(word_pos)
for i, one_sentence in enumerate(word_pos):
sentence_text = self.extract_one_sentence(one_sentence)
results = self.extract_one_word(one_sentence, sentence_text)
self.store_data.insert_data(self.cursor, results, self.language_name)
print('line %d, batch %d for %s written succeed' % (line_no, i, self.language_name))
line_no += 1
print(' all written succeed for corpus of %s' % self.our_corpus_name)
def extract_one_sentence(self, sentence) -> str:
"""
This private method is mainly used to extract the sentence text.
an instance of udpipe Sentence:
Sentence(
comments=[
'# sent_id = 3',
'# text = 黄土高原严寒而漫长的冬天看来就要过去，但那真正温暖的春天还远远地没有到来。'],
words=[
Word(id=0, <root>),
Word(id=1,
form='黄土',
lemma='黄土',
xpostag='NNP',
upostag='PROPN',
head=3,
deprel='nmod',
misc='SpaceAfter=No'),
Word(id=2,
form='高原',
lemma='高原',
xpostag='NN',
upostag='NOUN',
head=3,
deprel='nmod',
misc='SpaceAfter=No'),
Word(id=3,
form='严寒',
lemma='严寒',
xpostag='NN',
upostag='NOUN',
head=22,
deprel='nsubj',
misc='SpaceAfter=No'),
omited by myself ])
:param sentence: udpipe Sentence
:return: str 黄土高原严寒而漫长的冬天看来就要过去，但那真正温暖的春天还远远地没有到来。
"""
comment = ''.join(sentence.comments)
try:
cs = re.findall(r'text = (.*)', comment)[0]
return cs
except Exception as e:
# TODO: need to write warning log
print('error: not find a sentence', e)
return ''
def extract_one_word(self, sentence, sentence_text: str) -> [TResult]:
"""
This private method is mainly used to extract one word and it's POS
:param sentence_text:
:param sentence:
:return: [TResult]
"""
r = []
for word in sentence.words:
if word.lemma and word.lemma not in ITrain.FILTER_WORD:
if word.lemma and word.upostag and sentence_text:
r.append(TResult(word.lemma, word.upostag, sentence_text))
return r
def word_segmentation(self, sentence) -> List[str]:
"""
:param sentence:
:return: word list
"""
sen_clean = self.clean_data(sentence)
if not sen_clean:
return []
word_pos = list(self.model.process(sen_clean))
words = []
for i, one_sentence in enumerate(word_pos):
sentence_text = self.extract_one_sentence(one_sentence)
results = self.extract_one_word(one_sentence, sentence_text)
words.extend([res.word for res in results])
return words
'rb') as f:
for line in f.readlines():
data.append(str(line, 'utf-8'))
model = 'german-hdt-ud-2.5-191206.udpipe'
m = Model('../udpipe_model/' + model)
print(model, "loaded successfully!")
all_pos = []
for line in data:
#print(line)
sent_pos = []
sents = list(m.process(line, out_format="conllu"))
conllu = "".join(sents)
parse_con = parse(conllu)
# iterate over each word and append the POS into a list,
for i in parse_con[0]:
#print(i)
sent_pos.append(i['upostag'])
# append sent pos to the the doc
all_pos.append(sent_pos)
# write pos list into a file
with open('de_pos', 'wb') as f:
from corpy.udpipe import Model
from corpy.udpipe import pprint
m = Model("/home/zglg/SLU/psd/pre-model/classical_chinese-kyoto-ud-2.5-191206.udpipe")
sents = list(m.process("我爱北京天安门. 天安门上好风景"))
pprint(sents)