print("平均同义层过滤后实体个数: %s"%(tf/qnum))
if __name__ == '__main__':
print(__doc__)
parser = OptionParser()
parser.add_option("-d", "--data", dest="data",help="选择数据集")
parser.add_option("-t", "--level1", dest="level1",help="同义层层数")
parser.add_option("-r", "--level2", dest="level2",help="同义层层数")
(options, args) = parser.parse_args()
print("载入数据")
questions = ""
if options.data == "ya":
questions = data.get_data()
elif options.data == "duc":
questions = data.get_duc()
else:
logging.error("载入数据出现错误")
sys.exit(1)
length = len(questions)
level1 = int(options.level1)
level2 = int(options.level2)
exp(questions,length,level1,level2)
print("数据集%s 数据长度 %s, 同义层层数 %s 关联层层数 %s"%(options.data,length,level1,level2))
def exp(questions, qnum):
tratio, tquantity, te, tf = 0, 0, 0, 0
for i in range(qnum):
print("问题 %s" % (i))
q = questions[i]
ose = dummy2(q, 250)
result = ose.extract()
ose.evaluation(result, 'dumm1')
if __name__ == '__main__':
print(__doc__)
parser = OptionParser()
parser.add_option("-d", "--data", dest="data", help="选择数据集")
(options, args) = parser.parse_args()
print("载入数据")
questions = ""
if options.data == "ya":
questions = data.get_data()
elif options.data == "duc":
questions = data.get_duc()
else:
logging.error("载入数据出现错误")
sys.exit(1)
exp(questions, len(questions))
from insummer.common_type import Question
from insummer.util import rule_based_sentence_cleaner as RBSC
from insummer.util import NLP
import random
def rand() :
return random.random()
nlp = NLP()
rbsc = RBSC()
print("读数据")
questions = get_duc()
def main():
cand_sent = []
origin_length = 0
new_length = 0
for question in questions:
for ans in question.get_nbest():
content = ans.get_content()
origin_length += nlp.sentence_length(content)
sents = nlp.sent_tokenize(content)