class Detector(object):
def __init__(self,
language_model_path='',
word_freq_path='',
custom_word_freq_path='',
custom_confusion_path='',
person_name_path='',
place_name_path='',
stopwords_path=''):
self.name = 'detector'
self.language_model_path = os.path.join(pwd_path, language_model_path)
self.word_freq_path = os.path.join(pwd_path, word_freq_path)
self.custom_word_freq_path = os.path.join(pwd_path,
custom_word_freq_path)
self.custom_confusion_path = os.path.join(pwd_path,
custom_confusion_path)
self.person_name_path = os.path.join(pwd_path, person_name_path)
self.place_name_path = os.path.join(pwd_path, place_name_path)
self.stopwords_path = os.path.join(pwd_path, stopwords_path)
self.is_char_error_detect = True
self.is_word_error_detect = True
self.initialized_detector = False
def initialize_detector(self):
t1 = time.time()
self.lm = kenlm.Model(self.language_model_path)
t2 = time.time()
logger.debug('Loaded language model: %s, spend: %s s' %
(self.language_model_path, str(t2 - t1)))
# 词、频数dict
self.word_freq = self.load_word_freq_dict(self.word_freq_path)
t3 = time.time()
logger.debug('Loaded word freq file: %s, size: %d, spend: %s s' %
(self.word_freq_path, len(self.word_freq), str(t3 - t2)))
# 自定义混淆集
self.custom_confusion = self._get_custom_confusion_dict(
self.custom_confusion_path)
t4 = time.time()
logger.debug('Loaded confusion file: %s, size: %d, spend: %s s' %
(self.custom_confusion_path, len(
self.custom_confusion), str(t4 - t3)))
# 自定义切词词典
self.custom_word_freq = self.load_word_freq_dict(
self.custom_word_freq_path)
self.person_names = self.load_word_freq_dict(self.person_name_path)
self.place_names = self.load_word_freq_dict(self.place_name_path)
self.stopwords = self.load_word_freq_dict(self.stopwords_path)
# 合并切词词典及自定义词典
self.custom_word_freq.update(self.person_names)
self.custom_word_freq.update(self.place_names)
self.custom_word_freq.update(self.stopwords)
self.word_freq.update(self.custom_word_freq)
t5 = time.time()
logger.debug('Loaded custom word file: %s, size: %d, spend: %s s' %
(self.custom_confusion_path, len(
self.custom_word_freq), str(t5 - t4)))
logger.debug('Loaded all word freq file done, size: %d' %
len(self.word_freq))
self.tokenizer = Tokenizer(dict_path=self.word_freq_path,
custom_word_freq_dict=self.custom_word_freq,
custom_confusion_dict=self.custom_confusion)
t6 = time.time()
logger.info('Loaded dict ok, spend: %s s' % str(t6 - t1))
self.initialized_detector = True
def check_detector_initialized(self):
if not self.initialized_detector:
self.initialize_detector()
@staticmethod
def load_word_freq_dict(path):
"""
加载切词词典
:param path:
:return:
"""
word_freq = {}
with codecs.open(path, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if line.startswith('#'):
continue
info = line.split()
if len(info) < 1:
continue
word = info[0]
# 取词频，默认1
freq = int(info[1]) if len(info) > 1 else 1
word_freq[word] = freq
return word_freq
def _get_custom_confusion_dict(self, path):
"""
取自定义困惑集
:param path:
:return: dict, {variant: origin}, eg: {"交通先行": "交通限行"}
"""
confusion = {}
with codecs.open(path, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if line.startswith('#'):
continue
info = line.split()
if len(info) < 2:
continue
variant = info[0]
origin = info[1]
freq = int(info[2]) if len(info) > 2 else 1
self.word_freq[origin] = freq
confusion[variant] = origin
return confusion
def set_language_model_path(self, path):
self.check_detector_initialized()
self.lm = kenlm.Model(path)
logger.info('Loaded language model: %s' % path)
def set_custom_confusion_dict(self, path):
self.check_detector_initialized()
custom_confusion = self._get_custom_confusion_dict(path)
self.custom_confusion.update(custom_confusion)
logger.info('Loaded confusion path: %s, size: %d' %
(path, len(custom_confusion)))
def set_custom_word(self, path):
self.check_detector_initialized()
word_freqs = self.load_word_freq_dict(path)
# 合并字典
self.custom_word_freq.update(word_freqs)
# 合并切词词典及自定义词典
self.word_freq.update(self.custom_word_freq)
self.tokenizer = Tokenizer(dict_path=self.word_freq_path,
custom_word_freq_dict=self.custom_word_freq,
custom_confusion_dict=self.custom_confusion)
for k, v in word_freqs.items():
self.set_word_frequency(k, v)
logger.info('Loaded custom word path: %s, size: %d' %
(path, len(word_freqs)))
def enable_char_error(self, enable=True):
"""
is open char error detect
:param enable:
:return:
"""
self.is_char_error_detect = enable
def enable_word_error(self, enable=True):
"""
is open word error detect
:param enable:
:return:
"""
self.is_word_error_detect = enable
def ngram_score(self, chars):
"""
取n元文法得分
:param chars: list, 以词或字切分
:return:
"""
self.check_detector_initialized()
return self.lm.score(' '.join(chars), bos=False, eos=False)
def ppl_score(self, words):
"""
取语言模型困惑度得分，越小句子越通顺
:param words: list, 以词或字切分
:return:
"""
self.check_detector_initialized()
return self.lm.perplexity(' '.join(words))
def word_frequency(self, word):
"""
取词在样本中的词频
:param word:
:return:
"""
self.check_detector_initialized()
return self.word_freq.get(word, 0)
def set_word_frequency(self, word, num):
"""
更新在样本中的词频
"""
self.check_detector_initialized()
self.word_freq[word] = num
return self.word_freq
@staticmethod
def _check_contain_error(maybe_err, maybe_errors):
"""
检测错误集合(maybe_errors)是否已经包含该错误位置（maybe_err)
:param maybe_err: [error_word, begin_pos, end_pos, error_type]
:param maybe_errors:
:return:
"""
error_word_idx = 0
begin_idx = 1
end_idx = 2
for err in maybe_errors:
if maybe_err[error_word_idx] in err[error_word_idx] and maybe_err[begin_idx] >= err[begin_idx] and \
maybe_err[end_idx] <= err[end_idx]:
return True
return False
def _add_maybe_error_item(self, maybe_err, maybe_errors):
"""
新增错误
:param maybe_err:
:param maybe_errors:
:return:
"""
if maybe_err not in maybe_errors and not self._check_contain_error(
maybe_err, maybe_errors):
maybe_errors.append(maybe_err)
@staticmethod
def _get_maybe_error_index(scores, ratio=0.6745, threshold=1.4):
"""
取疑似错字的位置，通过平均绝对离差（MAD）
:param scores: np.array
:param threshold: 阈值越小，得到疑似错别字越多
:return:
"""
scores = np.array(scores)
if len(scores.shape) == 1:
scores = scores[:, None]
median = np.median(scores, axis=0) # get median of all scores
margin_median = np.sqrt(np.sum((scores - median)**2,
axis=-1)) # deviation from the median
# 平均绝对离差值
med_abs_deviation = np.median(margin_median)
if med_abs_deviation == 0:
return []
y_score = ratio * margin_median / med_abs_deviation
# 打平
scores = scores.flatten()
maybe_error_indices = np.where((y_score > threshold)
& (scores < median))
# 取全部疑似错误字的index
return list(maybe_error_indices[0])
def detect(self, sentence):
"""
检测句子中的疑似错误信息，包括[词、位置、错误类型]
:param sentence:
:return: [error_word, begin_pos, end_pos, error_type]
"""
maybe_errors = []
if not sentence.strip():
return maybe_errors
self.check_detector_initialized()
# 文本归一化
sentence = uniform(sentence)
# 切词
tokens = self.tokenizer.tokenize(sentence)
# print(tokens)
# 自定义混淆集加入疑似错误词典
for confuse in self.custom_confusion:
idx = sentence.find(confuse)
if idx > -1:
maybe_err = [
confuse, idx, idx + len(confuse), error_type["confusion"]
]
self._add_maybe_error_item(maybe_err, maybe_errors)
if self.is_word_error_detect:
# 未登录词加入疑似错误词典
for word, begin_idx, end_idx in tokens:
# pass blank
if not word.strip():
continue
# punctuation
if word in PUNCTUATION_LIST:
continue
# pass num
if word.isdigit():
continue
# pass alpha
if is_alphabet_string(word.lower()):
continue
# in dict
if word in self.word_freq:
continue
maybe_err = [word, begin_idx, end_idx, error_type["word"]]
self._add_maybe_error_item(maybe_err, maybe_errors)
if self.is_char_error_detect:
# 语言模型检测疑似错误字
ngram_avg_scores = []
try:
for n in [2, 3]:
scores = []
for i in range(len(sentence) - n + 1):
word = sentence[i:i + n]
score = self.ngram_score(list(word))
scores.append(score)
if not scores:
continue
# 移动窗口补全得分
for _ in range(n - 1):
scores.insert(0, scores[0])
scores.append(scores[-1])
avg_scores = [
sum(scores[i:i + n]) / len(scores[i:i + n])
for i in range(len(sentence))
]
ngram_avg_scores.append(avg_scores)
# 取拼接后的ngram平均得分
sent_scores = list(
np.average(np.array(ngram_avg_scores), axis=0))
# 取疑似错字信息
for i in self._get_maybe_error_index(sent_scores):
maybe_err = [sentence[i], i, i + 1, error_type["char"]]
self._add_maybe_error_item(maybe_err, maybe_errors)
except IndexError as ie:
logger.warn("index error, sentence:" + sentence + str(ie))
except Exception as e:
logger.warn("detect error, sentence:" + sentence + str(e))
return sorted(maybe_errors, key=lambda k: k[1], reverse=False)
class Detector(object):
def __init__(self, language_model_path=config.language_model_path,
word_freq_path=config.word_freq_path,
custom_word_freq_path=config.custom_word_freq_path,
custom_confusion_path=config.custom_confusion_path,
person_name_path=config.person_name_path,
place_name_path=config.place_name_path,
stopwords_path=config.stopwords_path,
enable_rnnlm=False,
rnnlm_vocab_path=config.rnnlm_vocab_path,
rnnlm_model_dir=config.rnnlm_model_dir):
self.name = 'detector'
self.language_model_path = language_model_path
self.word_freq_path = word_freq_path
self.custom_word_freq_path = custom_word_freq_path
self.custom_confusion_path = custom_confusion_path
self.person_name_path = person_name_path
self.place_name_path = place_name_path
self.stopwords_path = stopwords_path
self.is_char_error_detect = True
self.is_word_error_detect = True
self.initialized_detector = False
self.enable_rnnlm = enable_rnnlm
self.rnnlm_vocab_path = rnnlm_vocab_path
self.rnnlm_model_dir = rnnlm_model_dir
def initialize_detector(self):
t1 = time.time()
if self.enable_rnnlm:
self.lm = LM(self.rnnlm_model_dir, self.rnnlm_vocab_path)
logger.debug('Loaded language model: %s, spend: %s s' % (self.rnnlm_model_dir, str(time.time() - t1)))
else:
try:
import kenlm
except ImportError:
raise ImportError('pycorrector dependencies are not fully installed, '
'they are required for statistical language model.'
'Please use "pip install kenlm" to install it, not support Win.'
'if you are Win, Please install tensorflow and set enable_rnnlm=True.')
self.lm = kenlm.Model(self.language_model_path)
logger.debug('Loaded language model: %s, spend: %s s' % (self.language_model_path, str(time.time() - t1)))
# 词、频数dict
t2 = time.time()
self.word_freq = self.load_word_freq_dict(self.word_freq_path)
t3 = time.time()
logger.debug('Loaded word freq file: %s, size: %d, spend: %s s' %
(self.word_freq_path, len(self.word_freq), str(t3 - t2)))
# 自定义混淆集
self.custom_confusion = self._get_custom_confusion_dict(self.custom_confusion_path)
t4 = time.time()
logger.debug('Loaded confusion file: %s, size: %d, spend: %s s' %
(self.custom_confusion_path, len(self.custom_confusion), str(t4 - t3)))
# 自定义切词词典
self.custom_word_freq = self.load_word_freq_dict(self.custom_word_freq_path)
self.person_names = self.load_word_freq_dict(self.person_name_path)
self.place_names = self.load_word_freq_dict(self.place_name_path)
self.stopwords = self.load_word_freq_dict(self.stopwords_path)
# 合并切词词典及自定义词典
self.custom_word_freq.update(self.person_names)
self.custom_word_freq.update(self.place_names)
self.custom_word_freq.update(self.stopwords)
self.word_freq.update(self.custom_word_freq)
t5 = time.time()
logger.debug('Loaded custom word file: %s, size: %d, spend: %s s' %
(self.custom_confusion_path, len(self.custom_word_freq), str(t5 - t4)))
self.tokenizer = Tokenizer(dict_path=self.word_freq_path, custom_word_freq_dict=self.custom_word_freq,
custom_confusion_dict=self.custom_confusion)
t6 = time.time()
logger.info('Loaded dict ok, spend: %s s' % str(t6 - t1))
self.initialized_detector = True
def check_detector_initialized(self):
if not self.initialized_detector:
self.initialize_detector()
@staticmethod
def load_word_freq_dict(path):
"""
加载切词词典
:param path:
:return:
"""
word_freq = {}
with codecs.open(path, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if line.startswith('#'):
continue
info = line.split()
if len(info) < 1:
continue
word = info[0]
# 取词频，默认1
freq = int(info[1]) if len(info) > 1 else 1
word_freq[word] = freq
return word_freq
def _get_custom_confusion_dict(self, path):
"""
取自定义困惑集
:param path:
:return: dict, {variant: origin}, eg: {"交通先行": "交通限行"}
"""
confusion = {}
with codecs.open(path, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if line.startswith('#'):
continue
info = line.split()
if len(info) < 2:
continue
variant = info[0]
origin = info[1]
freq = int(info[2]) if len(info) > 2 else 1
self.word_freq[origin] = freq
confusion[variant] = origin
return confusion
def set_language_model_path(self, path):
self.check_detector_initialized()
import kenlm
self.lm = kenlm.Model(path)
logger.info('Loaded language model: %s' % path)
def set_custom_confusion_dict(self, path):
self.check_detector_initialized()
custom_confusion = self._get_custom_confusion_dict(path)
self.custom_confusion.update(custom_confusion)
logger.info('Loaded confusion path: %s, size: %d' % (path, len(custom_confusion)))
def set_custom_word(self, path):
self.check_detector_initialized()
word_freqs = self.load_word_freq_dict(path)
# 合并字典
self.custom_word_freq.update(word_freqs)
# 合并切词词典及自定义词典
self.word_freq.update(self.custom_word_freq)
self.tokenizer = Tokenizer(dict_path=self.word_freq_path, custom_word_freq_dict=self.custom_word_freq,
custom_confusion_dict=self.custom_confusion)
for k, v in word_freqs.items():
self.set_word_frequency(k, v)
logger.info('Loaded custom word path: %s, size: %d' % (path, len(word_freqs)))
def enable_char_error(self, enable=True):
"""
is open char error detect
:param enable:
:return:
"""
self.is_char_error_detect = enable
def enable_word_error(self, enable=True):
"""
is open word error detect
:param enable:
:return:
"""
self.is_word_error_detect = enable
def ngram_score(self, chars):
"""
取n元文法得分
:param chars: list, 以词或字切分
:return:
"""
self.check_detector_initialized()
return self.lm.score(' '.join(chars), bos=False, eos=False)
def char_scores(self, chars):
"""
取RNN语言模型各字的得分
:param chars: list, 以字切分
:return: scores, list
"""
self.check_detector_initialized()
return self.lm.char_scores(chars)
def ppl_score(self, words):
"""
取语言模型困惑度得分，越小句子越通顺
:param words: list, 以词或字切分
:return:
"""
self.check_detector_initialized()
return self.lm.perplexity(' '.join(words))
def word_frequency(self, word):
"""
取词在样本中的词频
:param word:
:return:
"""
self.check_detector_initialized()
return self.word_freq.get(word, 0)
def set_word_frequency(self, word, num):
"""
更新在样本中的词频
"""
self.check_detector_initialized()
self.word_freq[word] = num
return self.word_freq
@staticmethod
def _check_contain_error(maybe_err, maybe_errors):
"""
检测错误集合(maybe_errors)是否已经包含该错误位置（maybe_err)
:param maybe_err: [error_word, begin_pos, end_pos, error_type]
:param maybe_errors:
:return:
"""
error_word_idx = 0
begin_idx = 1
end_idx = 2
for err in maybe_errors:
if maybe_err[error_word_idx] in err[error_word_idx] and maybe_err[begin_idx] >= err[begin_idx] and \
maybe_err[end_idx] <= err[end_idx]:
return True
return False
def _add_maybe_error_item(self, maybe_err, maybe_errors):
"""
新增错误
:param maybe_err:
:param maybe_errors:
:return:
"""
if maybe_err not in maybe_errors and not self._check_contain_error(maybe_err, maybe_errors):
maybe_errors.append(maybe_err)
@staticmethod
def _get_maybe_error_index(scores, ratio=0.6745, threshold=1.4):
"""
取疑似错字的位置，通过平均绝对离差（MAD）
:param scores: np.array
:param threshold: 阈值越小，得到疑似错别字越多
:return: 全部疑似错误字的index: list
"""
result = []
scores = np.array(scores)
if len(scores.shape) == 1:
scores = scores[:, None]
median = np.median(scores, axis=0) # get median of all scores
margin_median = np.sqrt(np.sum((scores - median) ** 2, axis=-1)) # deviation from the median
# 平均绝对离差值
med_abs_deviation = np.median(margin_median)
if med_abs_deviation == 0:
return result
y_score = ratio * margin_median / med_abs_deviation
# 打平
scores = scores.flatten()
maybe_error_indices = np.where((y_score > threshold) & (scores < median))
# 取全部疑似错误字的index
result = list(maybe_error_indices[0])
return result
@staticmethod
def _get_maybe_error_index_by_rnnlm(scores, n=3):
"""
取疑似错字的位置，通过平均值上下三倍标准差之间属于正常点
:param scores: list, float
:param threshold: 阈值越小，得到疑似错别字越多
:return: 全部疑似错误字的index: list
"""
std = np.std(scores, ddof=1)
mean = np.mean(scores)
down_limit = mean - n * std
upper_limit = mean + n * std
maybe_error_indices = np.where((scores > upper_limit) | (scores < down_limit))
# 取全部疑似错误字的index
result = list(maybe_error_indices[0])
return result
@staticmethod
def is_filter_token(token):
result = False
# pass blank
if not token.strip():
result = True
# pass punctuation
if token in PUNCTUATION_LIST:
result = True
# pass num
if token.isdigit():
result = True
# pass alpha
if is_alphabet_string(token.lower()):
result = True
return result
def detect(self, sentence):
"""
检测句子中的疑似错误信息，包括[词、位置、错误类型]
:param sentence:
:return: list[list], [error_word, begin_pos, end_pos, error_type]
"""
maybe_errors = []
if not sentence.strip():
return maybe_errors
# 初始化
self.check_detector_initialized()
# 文本归一化
sentence = uniform(sentence)
# 切词
tokens = self.tokenizer.tokenize(sentence)
# print(tokens)
# 自定义混淆集加入疑似错误词典
for confuse in self.custom_confusion:
idx = sentence.find(confuse)
if idx > -1:
maybe_err = [confuse, idx, idx + len(confuse), ErrorType.confusion]
self._add_maybe_error_item(maybe_err, maybe_errors)
if self.is_word_error_detect:
# 未登录词加入疑似错误词典
for word, begin_idx, end_idx in tokens:
# pass filter word
if self.is_filter_token(word):
continue
# pass in dict
if word in self.word_freq:
continue
maybe_err = [word, begin_idx, end_idx, ErrorType.word]
self._add_maybe_error_item(maybe_err, maybe_errors)
if self.is_char_error_detect:
# 语言模型检测疑似错误字
if self.enable_rnnlm:
scores = self.char_scores(sentence)
# 取疑似错字信息
for i in self._get_maybe_error_index_by_rnnlm(scores):
token = sentence[i]
# pass filter word
if self.is_filter_token(token):
continue
maybe_err = [token, i, i + 1, ErrorType.char] # token, begin_idx, end_idx, error_type
self._add_maybe_error_item(maybe_err, maybe_errors)
else:
try:
ngram_avg_scores = []
for n in [2, 3]:
scores = []
for i in range(len(sentence) - n + 1):
word = sentence[i:i + n]
score = self.ngram_score(list(word))
scores.append(score)
if not scores:
continue
# 移动窗口补全得分
for _ in range(n - 1):
scores.insert(0, scores[0])
scores.append(scores[-1])
avg_scores = [sum(scores[i:i + n]) / len(scores[i:i + n]) for i in range(len(sentence))]
ngram_avg_scores.append(avg_scores)
# 取拼接后的n-gram平均得分
sent_scores = list(np.average(np.array(ngram_avg_scores), axis=0))
# 取疑似错字信息
for i in self._get_maybe_error_index(sent_scores):
token = sentence[i]
# pass filter word
if self.is_filter_token(token):
continue
maybe_err = [token, i, i + 1, ErrorType.char] # token, begin_idx, end_idx, error_type
self._add_maybe_error_item(maybe_err, maybe_errors)
except IndexError as ie:
logger.warn("index error, sentence:" + sentence + str(ie))
except Exception as e:
logger.warn("detect error, sentence:" + sentence + str(e))
return sorted(maybe_errors, key=lambda k: k[1], reverse=False)
class NameSort(object):
def __init__(self,
word_freq_path=config.word_freq_path,
name_sort_path=ccm_conf.name_sort_path,
leader_job_path=ccm_conf.leader_job_path,
leader_job_freq_dict_path=ccm_conf.leader_job_freq_dict_path):
self.leader_job_freq_dict = Detector.load_word_freq_dict(
leader_job_freq_dict_path)
self.word_freq_path = word_freq_path
print(self.leader_job_freq_dict)
self.tokenizer = Tokenizer(
dict_path=self.word_freq_path,
custom_word_freq_dict=self.leader_job_freq_dict)
self.name_sort_path = name_sort_path
self.leader_job_path = leader_job_path
def is_filter_token(self, token):
result = False
# pass blank
if not token.strip():
result = True
# pass punctuation
if token in PUNCTUATION_LIST:
result = True
# pass num
if token.isdigit():
result = True
# pass alpha
if is_alphabet_string(token.lower()):
result = True
return result
def load_ccm_word_freq_dict(self, path):
"""
加载切词词典
:param path:
:return:
"""
word_freq = {}
with codecs.open(path, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if line.startswith('#'):
continue
info = line.split('+')
if len(info) < 1:
continue
word = info[0] # word为姓名
# 取词频，默认1 长度大于一时 freq=info[1]为顺序 否则定义为1
freq = int(info[1]) if len(info) > 1 else 1
word_freq[word] = freq
# print("++++" + str(word_freq))
return word_freq
def load_ccm_job_freq_dict(self, path):
"""
加载切词词典
:param path:
:return:
"""
word_freq = {}
with codecs.open(path, 'r', encoding='utf-8') as f:
for line in f:
line = line.strip()
if line.startswith('#') or not line:
continue
info = line.split('：')
if len(info) < 1:
continue
# print("dddddd")
word = info[0] # 名字-习近平
# print(word)
job = info[1] # 职务+称谓
# print(job)
s1 = job.split('？')
if len(s1) > 1:
# print(s1)
s2 = s1[0].split('、') # 将职务用、隔开
s3 = s1[1].split('、') # 将称谓用、隔开
# print(s2)
# print(s3)
b = {'1': s2, '2': s3}
else:
s2 = s1[0].split('、') # 将职务用、隔开
b = {'1': s2}
# 取词频，默认1
# freq = int(info[1]) if len(info) > 1 else 1
word_freq[word] = b
return word_freq
def ccm_sort(self, sentence):
"""
"""
# 加载排序词典
name_model = self.load_ccm_word_freq_dict(self.name_sort_path)
maybe_errors = []
if not sentence.strip():
return maybe_errors
# 文本归一化
sentence = uniform(sentence)
# 切词
tokens = self.tokenizer.tokenize(sentence)
print(tokens)
temp = None
error_list = []
correct_list = []
new = []
i = -1
for word, begin_idx, end_idx in tokens:
new.append(word)
i += 1
if word in LINK_WORD:
temp = None
if name_model.get(word):
if not temp:
temp = name_model.get(word)
continue
else:
if temp > name_model.get(word):
p = tokens[i]
tokens[i] = tokens[i - 2]
tokens[i - 2] = p
print(tokens[i][0])
print(tokens[i - 2][0])
correct_list.append((tokens[i][0], i))
correct_list.append((tokens[i - 2][0], i - 2))
error_list.append((tokens[i][0], i))
else:
pass
# print(tokens)
# correct_list.append((tokens[i][0]))
for word, p in correct_list:
new[p] = word
print(new)
print("ls:" + str(correct_list))
correct = ''.join(new)
print("correct:" + correct)
# print(error_list)
# print(tokens)
# print(tokens[0])
return sorted(maybe_errors, key=lambda k: k[1], reverse=False)
def name_job(self, sentence):
"""
"""
# 加载人名-职务词典
job_model = self.load_ccm_job_freq_dict(self.leader_job_path)
print(job_model)
maybe_errors = []
if not sentence.strip():
return maybe_errors
# 文本归一化
sentence = uniform(sentence)
# 切词
tokens = self.tokenizer.tokenize(sentence)
print(tokens)
# temp = None
error_list = []
correct_list = []
new = []
i = 0
j = 0
for word, begin_idx, end_idx in tokens:
if job_model.get(word):
print(i) # 如果找到人名了，那么现在的i就是该人名的坐标
a = job_model.get(word)
front = a.get('1')
temp_list = []
for x in range(j, i): # j就是起点坐标，i就是终点坐标
if self.leader_job_freq_dict.get(tokens[x][0]):
if tokens[x][0] not in front:
temp_list.append(tokens[x][0])
if temp_list:
error_list.append({word: temp_list})
else:
pass
j = i + 1 # 起点坐标变为上一个人坐标的下一位坐标
i += 1
print(error_list)