class pyltp_worker(object):
#初始化，创建实例，加载基础模型
def __init__(self, model_path):
self.LTP_MODEL_DIR = model_path
self.segmentor = Segmentor() #分词
self.postagger = Postagger() #词性标注
self.recognizer = NamedEntityRecognizer() #命名实体识别
self.parser = Parser() #依存句法分析
self.load_model()
#加载基础模型
def load_model(self):
self.cws_model_path = os.path.join(self.LTP_MODEL_DIR,
'cws.model') #分词模型路径
self.pos_model_path = os.path.join(self.LTP_MODEL_DIR,
'pos.model') #词性标注模型路径
self.ner_model_path = os.path.join(self.LTP_MODEL_DIR,
'ner.model') #命名实体识别模型路径
self.par_model_path = os.path.join(self.LTP_MODEL_DIR,
'parser.model') #依存句法分析模型路径
self.segmentor.load(self.cws_model_path) #加载cws模型
self.postagger.load(self.pos_model_path) #加载pos模型
self.recognizer.load(self.ner_model_path) #加载ner模型
self.parser.load(self.par_model_path) #加载parser模型
#释放实例
def end(self):
self.segmentor.release() #分词
self.postagger.release() #词性标注
self.recognizer.release() #命名实体识别
self.parser.release() #依存句法分析
#加入自定义词典
def add_cws_userdict(self, lexicon_path):
self.segmentor.load_with_lexicon(lexicon_path)
def add_pos_userdict(self, lexicon_path):
self.postagger.load_with_lexicon(lexicon_path)
def add_ner_userdict(self, lexicon_path):
self.recognizer.load_with_lexicon(lexicon_path)
def add_par_userdict(self, lexicon_path):
self.parser.load_with_lexicon(lexicon_path)
#分句。按照标点符号来分，返回句子列表。
def sentsplit(self, text):
sentences = SentenceSpliter.split(text)
sentences_list = list(sentences)
return sentences_list
#分词。返回词列表。
def cws(self, text):
words = self.segmentor.segment(text)
words_list = list(words)
return words_list
#词性标注。返回词性标注列表。
def pos(self, words):
postags = self.postagger.postag(words)
postags_list = list(postags)
return postags_list
#命名实体识别。返回命名实体类型列表。
def ner(self, words, postags):
nertags = self.recognizer.recognize(words, postags)
nertags_list = list(nertags)
return nertags_list
#依存句法分析。
def par(self, words, postags):
arcs = self.parser.parse(words, postags)
pr_list = []
word_list = []
word_pos_list = []
source_list = []
source_pos_list = []
relation_list = []
for i, k in enumerate(arcs):
word = words[i]
word_pos = postags[i]
source = words[k.head - 1]
source_pos = postags[k.head - 1]
relation = k.relation
word_list.append(word)
word_pos_list.append(word_pos)
source_list.append(source)
source_pos_list.append(source_pos)
relation_list.append(relation)
pr_list.append([word, word_pos, source, source_pos, relation])
df_list = [
word_list, word_pos_list, source_list, source_pos_list,
relation_list
]
return pr_list, df_list, arcs
