分类： Python/Ruby
2021-10-06 16:18:01
import time
import re
class Segment:
# 数据成员
sentence = ""
MaxLen = 0
pos = 0
len = 0
result_MM = "" # 存放MM分词结果
result_RMM = "" # 存放RMM分词结果
final_res = ""
dict = []
# 构造函数
def __init__(self, sentence, MaxLen):
self.sentence = sentence
self.MaxLen = MaxLen
self.pos = 0
self.len = self.MaxLen
self.result_MM = ""
self.readDict()
# 读字典
def readDict(self):
f = open("chineseDic.txt", "r", encoding="utf-8")
lines = f.readlines()
for line in lines:
# print(line)
words = line.split(",")
self.dict.append(words[0])
# 正向最大匹配
def MM(self, nLen, nPos):
length = len(self.sentence)
if (nPos > length):
return
substr = self.sentence[nPos:nPos + nLen]
if substr in self.dict:
self.result_MM = self.result_MM + substr + "/ "
nPos = nPos + nLen
nLen = self.MaxLen
self.MM(nLen, nPos)
elif nLen > 1:
nLen = nLen - 1
self.MM(nLen, nPos)
else:
self.result_MM = self.result_MM + substr + "/ "
nPos = nPos + 1
nLen = self.MaxLen
self.MM(nLen, nPos)
# 逆向最大匹配
def RMM(self, nLen, nPos):
if (nPos < 0):
return
substr = self.sentence[nPos - nLen:nPos]
if substr in self.dict:
self.result_RMM = self.result_RMM + "/" + substr
nPos = nPos - nLen
nLen = self.MaxLen
self.RMM(nLen, nPos)
elif nLen > 1:
nLen = nLen - 1
self.RMM(nLen, nPos)
else:
self.result_RMM = self.result_RMM + substr + "/"
nPos = nPos - 1
nLen = self.MaxLen
self.RMM(nLen, nPos)
def getMMResult(self):
return self.result_MM
def getRMMResult(self):
return self.result_RMM
def getFinalResult(self):
return self.final_res
def printFinalResult(self):
print("正向最大匹配结果：")
seg_res_MM = self.result_MM.replace(" ", "")
print(seg_res_MM)
seg_list_MM = seg_res_MM.split('/')
del seg_list_MM[-1] # 外汇跟单gendan5.com由于按照'/'分割，所以最后会多出一个''，删去
print(seg_list_MM)
print("逆向最大匹配结果：")
seg_res_RMM = self.result_RMM.replace(" ", "")
print(seg_res_RMM)
seg_list_RMM = list(reversed(seg_res_RMM.split('/')))
del seg_list_RMM[0]
del seg_list_RMM[-1]
print(seg_list_RMM)
len_MM = len(seg_list_MM)
len_RMM = len(seg_list_RMM)
flag = 1
for i in range(0, min(len_MM, len_RMM)):
if seg_list_MM[i] != seg_list_RMM[i]:
print("两次分词结果不一致。")
flag = 0
break
if (flag):
print("两次分词结果一致。")
print("最终的分词结果为：")
self.final_res = self.result_MM
print(self.final_res)
def to_region(segmentation):
region = []
start = 1
for word in re.compile("\\s+").split(segmentation.strip()): # 空格，回车，换行等空白符
end = start + len(word) - 2
region.append((start, end))
start = end + 1
return region
def PRF(target, pred):
t_set, p_set = set(target), set(pred)
target_num = len(t_set)
pred_num = len(p_set)
cap_num = len(t_set & p_set)
p = cap_num / pred_num
r = cap_num / target_num
f = 2 * p * r / (p + r)
print("P =", p)
print("R =", r)
print("F1 =", f)
if __name__ == '__main__':
test_str = '在这一年中，中国的改革开放和现代化建设继续向前迈进。国民经济保持了“高增长、低通胀”的良好发展态势。农业生产再次获得好的收成，企业改革继续深化，人民生活进一步改善。对外经济技术合作与交流不断扩大。'
seg = Segment(test_str, 3)
time_start = time.time()
seg.MM(3, 0)
seg.RMM(3, len(test_str))
time_end = time.time()
seg.printFinalResult()
print('分词时间：', time_end - time_start, 's')
target_str = "在/ 这/ 一/ 年/ 中/ ，/ 中国/ 的/ 改革/ 开放/ 和/ 现代化/ 建设/ 继续/ 向前/ 迈进/ 。/ 国民经济/ 保持/ 了/ “/ 高/ 增长/ 、/ 低/ 通胀/ ”/ 的/ 良好/ 发展/ 态势/ 。/ 农业/ 生产/ 再次/ 获得/ 好/ 的/ 收成/ ，/ 企业/ 改革/ 继续/ 深化/ ，/ 人民/ 生活/ 进一步/ 改善/ 。/ 对外/ 经济/ 技术/ 合作/ 与/ 交流/ 不断/ 扩大/ 。/"
re_pred = to_region(seg.getFinalResult())
re_target = to_region(target_str)
# 每个单词按它在文本中的起止位置可记作区间[i, j]
print("分词结果：", re_pred)
print("标准答案：", re_target)
PRF(re_target, re_pred)
