非结构化商业文本信息中隐私信息识别Baseline

简介

最近，CCF大数据与计算智能大赛出了好几个赛题。

CCF BDCI比赛链接：https://www.datafountain.cn/special/BDCI2020
比赛baseline汇总：https://github.com/datawhalechina/competition-baseline

我报名了这个《非结构化商业文本信息中隐私信息识别》，赛题详情可见https://www.datafountain.cn/competitions/472，看到数据与评测说明时，这不就是NER吗？然后我又想到了苏大神的bert4keras（https://github.com/bojone/bert4keras），感觉bert4keras简直就是NLP领域里baseline首选。

关于数据说明，官网已经列了，这里不在详细，可看下图

数据预处理

我们来看下文本长度分布：

import glob
import numpy as np
import matplotlib.pyplot as plt
file_list = glob.glob('./data/train/data/*.txt')
file_sizes = []
for file in file_list:with open(file,"r") as f:file_sizes.append(len(f.read()))
plt.hist(x = file_sizes, bins = 20, color = 'steelblue', edgecolor = 'black', rwidth=0.7)
plt.show()

看上图，大部分的文本长度在200以内，少数比较长，适当设置文本长度来截断长文本

然后来看下标签数据：

label_file_list = glob.glob('./data/train/label/*.csv')
import pandas as pd
labels = dict()
for label_file in label_file_list:df = pd.read_csv(label_file, sep=",",encoding="utf-8")count_df = df['Category'].value_counts()for label in list(count_df.index):if labels.get(label) is None:labels[label] = count_df[label]else:labels[label] += count_df[label]
labels

可以看到，标签数据是严重不平衡的，position类别是最多的，有3580条，最少的vx只有19条，在标签数据不平衡的情况下如何改进baseline是之后要放重点的地方。

我们需要把数据整理成以下格式：

所以一步步来，先将文本数据分句处理,再按文本长度阈值分割文本，按"字标志属性"统一写入新的文件中，我在这里将数据分了5折，用于后续交叉训练：

def _cut(sentence):"""将一段文本切分成多个句子:param sentence::return:"""new_sentence = []sen = []for i in sentence:if i in ['。', '！', '？', '?'] and len(sen) != 0:sen.append(i)new_sentence.append("".join(sen))sen = []continuesen.append(i)if len(new_sentence) <= 1: # 一句话超过max_seq_length且没有句号的，用","分割，再长的不考虑了。new_sentence = []sen = []for i in sentence:if i.split(' ')[0] in ['，', ','] and len(sen) != 0:sen.append(i)new_sentence.append("".join(sen))sen = []continuesen.append(i)if len(sen) > 0:  # 若最后一句话无结尾标点，则加入这句话new_sentence.append("".join(sen))return new_sentence

def cut_test_set(text_list,len_treshold):cut_text_list = []cut_index_list = []for text in text_list:temp_cut_text_list = []text_agg = ''if len(text) < len_treshold:temp_cut_text_list.append(text)else:sentence_list = _cut(text)  # 一条数据被切分成多句话for sentence in sentence_list:if len(text_agg) + len(sentence) < len_treshold:text_agg += sentenceelse:temp_cut_text_list.append(text_agg)text_agg = sentencetemp_cut_text_list.append(text_agg)  # 加上最后一个句子cut_index_list.append(len(temp_cut_text_list))cut_text_list += temp_cut_text_listreturn cut_text_list, cut_index_list

def process_one(text_file, lable_file, w_path_, text_length):with open(text_file,"r") as f:text = f.read()lines, line_len = cut_test_set([text],text_length)df = pd.read_csv(lable_file, sep=",",encoding="utf-8")q_dic = dict()for index, row in df.iterrows():cls = row[1]start_index = row[2]end_index = row[3]length = end_index - start_index+1for r in range(length):if r == 0:q_dic[start_index] = ("B-%s" % cls)else:q_dic[start_index + r] = ("I-%s" % cls)i = 0for idx, line in enumerate(lines):with codecs.open(w_path_, "a+", encoding="utf-8") as w:for str_ in line:if str_ is " " or str_ == "" or str_ == "\n" or str_ == "\r":passelse:if i in q_dic:tag = q_dic[i]else:tag = "O"  # 大写字母Ow.write('%s %s\n' % (str_, tag))i+=1w.write('\n')

import glob
import numpy as np
from sklearn.model_selection import train_test_split,KFold
import os
import pandas as pd
import codecs
file_list = glob.glob('./data/train/data/*.txt')
kf = KFold(n_splits=5, shuffle=True, random_state=999).split(file_list)
file_list = np.array(file_list)
#设置样本长度
text_length = 250
for i, (train_fold, test_fold) in enumerate(kf):print(len(file_list[train_fold]),len(file_list[test_fold]))train_filelist = list(file_list[train_fold])val_filelist = list(file_list[test_fold])# train_filetrain_w_path = f'./data/train_{i}.txt'for file in train_filelist:if not file.endswith('.txt'):continuefile_name = file.split(os.sep)[-1].split('.')[0]label_file = os.path.join("./data/train/label", "%s.csv" % file_name)process_one(file, label_file, train_w_path, text_length)# val_fileval_w_path = f'./data/val_{i}.txt'for file in val_filelist:if not file.endswith('.txt'):continuefile_name = file.split(os.sep)[-1].split('.')[0]label_file = os.path.join("./data/train/label", "%s.csv" % file_name)process_one(file, label_file, val_w_path, text_length)

模型构建与训练

接下去就是建模和训练了。

import os
import tensorflow as tf
import keras
import bert4keras
import numpy as np
from bert4keras.backend import keras, K
from bert4keras.models import build_transformer_model
from bert4keras.tokenizers import Tokenizer
from bert4keras.optimizers import Adam
from bert4keras.snippets import sequence_padding, DataGenerator
from bert4keras.snippets import open, ViterbiDecoder, to_array
from bert4keras.layers import ConditionalRandomField
from keras.layers import Dense,Bidirectional, LSTM,Dropout
from keras.models import Model
from tqdm import tqdm
maxlen = 250
epochs = 10
# batch_size = 8
bert_layers = 12
learing_rate = 3e-5  # bert_layers越小，学习率应该要越大
crf_lr_multiplier = 1500  # 必要时扩大CRF层的学习率 #500,1500# bert配置
config_path = './publish/bert_config.json'
checkpoint_path = './publish/bert_model.ckpt'
dict_path = './publish/vocab.txt'def load_data(filename):D = []with open(filename, encoding='utf-8') as f:f = f.read()for l in f.split('\n\n'):if not l:continued, last_flag = [], ''for c in l.split('\n'):try:char, this_flag = c.split(' ')except:print(c)continueif this_flag == 'O' and last_flag == 'O':d[-1][0] += charelif this_flag == 'O' and last_flag != 'O':d.append([char, 'O'])elif this_flag[:1] == 'B':d.append([char, this_flag[2:]])else:d[-1][0] += charlast_flag = this_flagD.append(d)return D# 建立分词器
tokenizer = Tokenizer(dict_path, do_lower_case=True)# 类别映射labels = ['name','movie','organization','position','company','game','book','address','government','scene','email','mobile','QQ','vx']id2label = dict(enumerate(labels))
label2id = {j: i for i, j in id2label.items()}
num_labels = len(labels) * 2 + 1class data_generator(DataGenerator):"""数据生成器"""def __iter__(self, random=False):batch_token_ids, batch_segment_ids, batch_labels = [], [], []for is_end, item in self.sample(random):token_ids, labels = [tokenizer._token_start_id], [0]for w, l in item:w_token_ids = tokenizer.encode(w)[0][1:-1]if len(token_ids) + len(w_token_ids) < maxlen:token_ids += w_token_idsif l == 'O':labels += [0] * len(w_token_ids)else:B = label2id[l] * 2 + 1I = label2id[l] * 2 + 2labels += ([B] + [I] * (len(w_token_ids) - 1))else:breaktoken_ids += [tokenizer._token_end_id]labels += [0]segment_ids = [0] * len(token_ids)batch_token_ids.append(token_ids)batch_segment_ids.append(segment_ids)batch_labels.append(labels)if len(batch_token_ids) == self.batch_size or is_end:batch_token_ids = sequence_padding(batch_token_ids)batch_segment_ids = sequence_padding(batch_segment_ids)batch_labels = sequence_padding(batch_labels)yield [batch_token_ids, batch_segment_ids], batch_labelsbatch_token_ids, batch_segment_ids, batch_labels = [], [], []def bertmodel():model = build_transformer_model(config_path,checkpoint_path,)output_layer = 'Transformer-%s-FeedForward-Norm' % (bert_layers -1)output = model.get_layer(output_layer).outputoutput = Dense(num_labels)(output) # 27分类CRF = ConditionalRandomField(lr_multiplier=crf_lr_multiplier)output = CRF(output)model = Model(model.input, output)
#     model.summary()model.compile(loss=CRF.sparse_loss,optimizer=Adam(learing_rate),metrics=[CRF.sparse_accuracy])return model,CRFclass NamedEntityRecognizer(ViterbiDecoder):"""命名实体识别器"""def recognize(self, text):tokens = tokenizer.tokenize(text)mapping = tokenizer.rematch(text, tokens)token_ids = tokenizer.tokens_to_ids(tokens)segment_ids = [0] * len(token_ids)token_ids, segment_ids = to_array([token_ids], [segment_ids])nodes = model.predict([token_ids, segment_ids])[0]labels = self.decode(nodes)entities, starting = [], Falsefor i, label in enumerate(labels):if label > 0:if label % 2 == 1:starting = Trueentities.append([[i], id2label[(label - 1) // 2]])elif starting:entities[-1][0].append(i)else:starting = Falseelse:starting = Falsereturn [(text[mapping[w[0]][0]:mapping[w[-1]][-1] + 1], l)for w, l in entities]def evaluate(data):"""评测函数"""X, Y, Z = 1e-10, 1e-10, 1e-10for d in tqdm(data):text = ''.join([i[0] for i in d])R = set(NER.recognize(text)) # 预测T = set([tuple(i) for i in d if i[1] != 'O']) #真实X += len(R & T) Y += len(R) Z += len(T)precision, recall =  X / Y, X / Zf1 = 2*precision*recall/(precision+recall)return f1, precision, recallclass Evaluator(keras.callbacks.Callback):def __init__(self,valid_data, mode=0):self.best_val_f1 = 0self.valid_data = valid_dataself.mode=mode  # k折的时候记录第几折def on_epoch_end(self, epoch, logs=None):trans = K.eval(CRF.trans)NER.trans = trans
#         print(NER.trans)f1, precision, recall = evaluate(self.valid_data)# 保存最优if f1 >= self.best_val_f1:self.best_val_f1 = f1model.save_weights('./best_bilstm_model_{}.weights'.format(self.mode))logger.info('valid:  f1: %.5f, precision: %.5f, recall: %.5f, best f1: %.5f\n' %(f1, precision, recall, self.best_val_f1))batch_size = 12train_data = load_data(f'./data/train_0.txt')
valid_data = load_data(f'./data/val_0.txt')
model,CRF = bertmodel()
NER = NamedEntityRecognizer(trans=K.eval(CRF.trans), starts=[0], ends=[0])evaluator = Evaluator(valid_data)
train_generator = data_generator(train_data, batch_size)model.fit_generator(train_generator.forfit(),steps_per_epoch=len(train_generator),epochs=epochs,callbacks=[evaluator])

预测

这里要注意一下，

1.属性要小写，官网上列举的样例是大写，我一开始也改成大写了，然后提交的时候老是报错，后来还是网友指点才改成小写的。

2.预测end的时候，要记得-1，第一次成功提交的时候，分数只有0.003...，看到这个分数，肯定是起始/终止位置搞错了，很快就发现了。

3.df.to_csv()的时候，encoding要设置为utf-8-sig，我一开始设置的utf-8，报错，网上查了说要加-sig

def test_predict(data, NER_):test_ner =[]    for text in tqdm(data):cut_text_list, cut_index_list = cut_test_set([text],maxlen)posit = 0item_ner = []index =1for str_ in cut_text_list:ner_res  = NER_.recognize(str_)for tn in ner_res:ans = {}ans["label_type"] = tn[1]ans['overlap'] = "T" + str(index)ans["start_pos"] = text.find(tn[0],posit)ans["end_pos"] = ans["start_pos"] + len(tn[0])-1posit = ans["end_pos"]ans["res"] = tn[0]item_ner.append(ans)index +=1test_ner.append(item_ner)    return test_nertest_files = os.listdir("./data/test")
ids = []
starts = []
ends = []
labels=[]
ress = []
for file in test_files:if not file.endswith(".txt"):continueid_ = file.split('.')[0]with codecs.open("./data/test/"+file, "r", encoding="utf-8") as f:line = f.readlines()aa = test_predict(line, NER)for line in aa[0]:ids.append(id_)labels.append(line['label_type'])starts.append(line['start_pos'])ends.append(line['end_pos'])ress.append(line['res'])df=pd.DataFrame({"ID":ids, "Category":labels,"Pos_b":starts,"Pos_e":ends,"Privacy":ress})
df.to_csv("predict.csv", encoding="utf-8-sig", sep=',',index=False)

其实这个baseline是深度之眼中医药说明书实体识别赛道指导班的导师GAuss老师提供的初版，我针对《非结构化商业文本信息中隐私信息识别》这个赛题，做了些改动，主要就是数据处理方面的改动。

OK，接下去的提升就要靠自己了，加油！

如果有兴趣，可以加入比赛指导班，各种比赛提分技巧在分享。前者是天池上的中药说明书实体识别赛道指导班，虽然比赛结束了，但是课程还在，数据也有，还是可以学习，也能应用到其他NER比赛中，后者是非结构化商业文本信息中隐私信息识别指导班，即将开班，这个比赛现在还在进行中，想要0基础开打或提升分数的朋友们赶紧加入吧。