python实现命名实体识别指标（实体级别）

pre = \"0 0 B_SONG I_SONG I_SONG 0 B_SONG I_SONG I_SONG 0 0 B_SINGER I_SINGER I_SINGER 0 O O O B_ALBUM I_ALBUM I_ALBUM O O B_TAG I_TAG I_TAG O\"true = \"0 0 B_SONG I_SONG I_SONG 0 0 0 0 0 0 B_SINGER I_SINGER I_SINGER 0 O O O B_ALBUM I_ALBUM I_ALBUM O O B_TAG I_TAG I_TAG O\"tags = [(\"B_SONG\",\"I_SONG\"),(\"B_SINGER\",\"I_SINGER\"),(\"B_ALBUM\",\"I_ALBUM\"),(\"B_TAG\",\"I_TAG\")]def find_tag(labels,B_label=\"B_SONG\",I_label=\"I_SONG\"):result = []if isinstance(labels,str): # 如果labels是字符串labels = labels.strip().split() # 将labels进行拆分labels = [\"O\" if label ==\"0\" else label for label in labels] # 如果标签是O就就是O，否则就是label# print(labels)for num in range(len(labels)): # 遍历Labelsif labels[num] == B_label:song_pos0 = num # 记录B_SONG的位置if labels[num] == I_label and labels[num-1] == B_label: # 如果当前lable是I_SONG且前一个是B_SONGlenth = 2 # 当前长度为2for num2 in range(num,len(labels)): # 从该位置开始继续遍历if labels[num2] == I_label and labels[num2-1] == I_label: # 如果当前位置和前一个位置是I_SONGlenth += 1 # 长度+1if labels[num2] == \"O\": # 如果当前标签是Oresult.append((song_pos0,lenth)) #z则取得B的位置和长度break # 退出第二个循环return resultdef find_all_tag(labels):result = {}for tag in tags:res = find_tag(labels,B_label=tag[0],I_label=tag[1])result[tag[0].split(\"_\")[1]] = res # 将result赋值给就标签return resultres = find_all_tag(pre)

结果：

{\’ALBUM\’: [(18, 3)], \’SINGER\’: [(11, 3)], \’SONG\’: [(2, 3), (6, 3)], \’TAG\’: [(23, 3)]}

接下来计算精确率precision、召回率（查全率）recall、F1：

def precision(pre_labels,true_labels):\'\'\':param pre_tags: list:param true_tags: list:return:\'\'\'pre = []if isinstance(pre_labels,str):pre_labels = pre_labels.strip().split() # 字符串转换为列表pre_labels = [\"O\" if label ==\"0\" else label for label in pre_labels]if isinstance(true_labels,str):true_labels = true_labels.strip().split()true_labels = [\"O\" if label ==\"0\" else label for label in true_labels]pre_result = find_all_tag(pre_labels) # pre_result是一个字典，键是标签，值是一个元组，第一位是B的位置，第二位是长度for name in pre_result: # 取得键，也就是标签for x in pre_result[name]: # 取得值：也就是元组，注意元组可能有多个if x: # 如果x存在if pre_labels[x[0]:x[0]+x[1]] == true_labels[x[0]:x[0]+x[1]]: # 判断对应位置的每个标签是否一致pre.append(1) # 一致则结果添加1else:pre.append(0) # 不一致则结果添加0return sum(pre)/len(pre) #为1的个数/总个数def recall(pre_labels,true_labels):\'\'\':param pre_tags: list:param true_tags: list:return:\'\'\'recall = []if isinstance(pre_labels,str):pre_labels = pre_labels.strip().split()pre_labels = [\"O\" if label ==\"0\" else label for label in pre_labels]if isinstance(true_labels,str):true_labels = true_labels.strip().split()true_labels = [\"O\" if label ==\"0\" else label for label in true_labels]true_result = find_all_tag(true_labels)for name in true_result: # 取得键，也就是标签，这里注意和计算precision的区别，遍历的是真实标签列表for x in true_result[name]: # 以下的基本差不多if x:if pre_labels[x[0]:x[0]+x[1]] == true_labels[x[0]:x[0]+x[1]]:recall.append(1)else:recall.append(0)return sum(recall)/len(recall)def f1_score(precision,recall):return (2*precision*recall)/(precision+recall) # 有了precision和recall，计算F1就简单了if __name__ == \'__main__\':precision = precision(pre,true)recall = recall(pre,true)f1 = f1_score(precision,recall)print(precision)print(recall)print(f1)

结果：

0.8

1.0

0.888888888888889

参考：http://www.manongjc.com/detail/15-ochyrivhdccrvka.html