基于tensorflow的bilstm_crf的命名实体识别（数据集是msra命名实体识别数据集）

github地址：https://github.com/taishan1994/tensorflow-bilstm-crf

1、熟悉数据

msra数据集总共有三个文件：

train.txt：部分数据

当/o 希望工程/o 救助/o 的/o 百万/o 儿童/o 成长/o 起来/o ，/o 科教/o 兴/o 国/o 蔚然成风/o 时/o ，/o 今天/o 有/o 收藏/o 价值/o 的/o 书/o 你/o 没/o 买/o ，/o 明日/o 就/o 叫/o 你/o 悔不当初/o ！/o
藏书/o 本来/o 就/o 是/o 所有/o 传统/o 收藏/o 门类/o 中/o 的/o 第一/o 大户/o ，/o 只是/o 我们/o 结束/o 温饱/o 的/o 时间/o 太/o 短/o 而已/o 。/o
因/o 有关/o 日/ns 寇/o 在/o 京/ns 掠夺/o 文物/o 详情/o ，/o 藏/o 界/o 较为/o 重视/o ，/o 也是/o 我们/o 收藏/o 北京/ns 史料/o 中/o 的/o 要件/o 之一/o 。/o 

test.txt：部分数据

今天的演讲会是由哈佛大学费正清东亚研究中心主任傅高义主持的。

testright.txt：部分数据

今天的演讲会是由/o 哈佛大学费正清东亚研究中心/nt 主任/o 傅高义/nr 主持的。/o 

2、数据预处理

代码：

#coding:utf-8
import os
BASE_DIR = os.path.dirname(os.path.dirname(os.path.abspath(__file__))) #当前程序上上一级目录，这里为ner
import sys
sys.path.append(BASE_DIR)
print(BASE_DIR)
import codecs
import re
import pandas as pd
import numpy as np
from config.globalConfig import *

#============================第一步：给每一个字打上标签===================================
def wordtag():
    #用utf-8-sig编码的原因是文本保存时包含了BOM（Byte Order Mark，字节顺序标记，\ufeff出现在文本文件头部，为了去掉这个
    input_data = codecs.open(os.path.join(PATH,'data/msra/train.txt'),'r','utf-8-sig') #一般使用codes打开文件，不会出现编码问题
    output_data = codecs.open(os.path.join(PATH,'data/msra/wordtag.txt'),'w','utf-8')
    for line in input_data.readlines():
        #line=re.split('[，。；！：？、‘’“”]/[o]'.decode('utf-8'),line.strip())
        line = line.strip().split()

        if len(line)==0: #过滤掉''
            continue
        for word in line: #遍历列表中的每一个词
            word = word.split('/') #['希望工程', 'o']，每个词是这样的了
            if word[1]!='o': #如果不是o
                if len(word[0])==1: #如果是一个字，那么就直接给标签
                    output_data.write(word[0]+"/B_"+word[1]+" ")
                elif len(word[0])==2: #如果是两个字则拆分给标签
                    output_data.write(word[0][0]+"/B_"+word[1]+" ")
                    output_data.write(word[0][1]+"/E_"+word[1]+" ")
                else: #如果两个字以上，也是拆开给标签
                    output_data.write(word[0][0]+"/B_"+word[1]+" ")
                    for j in word[0][1:len(word[0])-1]:
                        output_data.write(j+"/M_"+word[1]+" ")
                    output_data.write(word[0][-1]+"/E_"+word[1]+" ")
            else: #如果表示前是o的话，将拆开为字并分别给标签/o
                for j in word[0]:
                    output_data.write(j+"/o"+" ")
        output_data.write('\n')
    input_data.close()
    output_data.close()

#============================第二步：构建二维字列表以及其对应的二维标签列表===================================
wordtag()
datas = list()
labels = list()
linedata=list()
linelabel=list()

# 0表示补全的id
tag2id = {'' :0,
'B_ns' :1,
'B_nr' :2,
'B_nt' :3,
'M_nt' :4,
'M_nr' :5,
'M_ns' :6,
'E_nt' :7,
'E_nr' :8,
'E_ns' :9,
'o': 10}

id2tag = {0:'' ,
1:'B_ns' ,
2:'B_nr' ,
3:'B_nt' ,
4:'M_nt' ,
5:'M_nr' ,
6:'M_ns' ,
7:'E_nt' ,
8:'E_nr' ,
9:'E_ns' ,
10: 'o'}

input_data = codecs.open(os.path.join(PATH,'data/msra/wordtag.txt'),'r','utf-8')
for line in input_data.readlines(): #每一个line实际上是这样子的：当/o 希/o 望/o 工/o 程/o 救/o 助/o  注意最后多了个''
  line=re.split('[，。；！：？、‘’“”]/[o]'.encode("utf-8").decode('utf-8'),line.strip()) #a按指定字符划分字符串
  for sen in line: #
      sen = sen.strip().split() #每一个字符串列表再按照弄空格划分，然后每个字是：当/o
      if len(sen)==0: #过滤掉为空的
          continue
      linedata=[]
      linelabel=[]
      num_not_o=0
      for word in sen: #遍历每一个字
          word = word.split('/') #第一位是字，第二位是标签
          linedata.append(word[0]) #加入到字列表
          linelabel.append(tag2id[word[1]]) #加入到标签列表，要转换成对应的id映射

          if word[1]!='o':
              num_not_o+=1 #记录标签不是o的字的个数
      if num_not_o!=0: #如果num_not_o不为0，则表明当前linedata和linelabel有要素
          datas.append(linedata)
          labels.append(linelabel)

input_data.close()
print(len(datas))
print(len(labels))

#============================第三步：构建word2id以及id2word===================================
#from compiler.ast import flatten (在python3中不推荐使用)，我们自己定义一个
def flat2gen(alist):
  for item in alist:
    if isinstance(item, list):
      for subitem in item: yield subitem
    else:
      yield item
all_words = list(flat2gen(datas)) #获得包含所有字的列表
sr_allwords = pd.Series(all_words) #转换为pandas中的Series
sr_allwords = sr_allwords.value_counts() #统计每一个字出现的次数，相当于去重
set_words = sr_allwords.index #每一个字就是一个index，这里的字按照频数从高到低排序了
set_ids = range(1, len(set_words)+1) #给每一个字一个id映射，注意这里是从1开始，因为我们填充序列时使用0填充的，也就是id为0的已经被占用了
word2id = pd.Series(set_ids, index=set_words) #字 id
id2word = pd.Series(set_words, index=set_ids) #id 字

word2id["unknow"] = len(word2id)+1 #加入一个unknow，如果没出现的字就用unknow的id代替

#============================第四步：定义序列最大长度，对序列进行处理==================================
max_len = MAX_LEN #句子的最大长度
def X_padding(words):
  """把 words 转为 id 形式，并自动补全位 max_len 长度。"""
  ids = list(word2id[words])
  if len(ids) >= max_len:  # 长则弃掉
      return ids[:max_len]
  ids.extend([0]*(max_len-len(ids))) # 短则补全
  return ids

def y_padding(ids):
  """把 tags 转为 id 形式， 并自动补全位 max_len 长度。"""
  if len(ids) >= max_len:  # 长则弃掉
      return ids[:max_len]
  ids.extend([0]*(max_len-len(ids))) # 短则补全
  return ids

def get_true_len(ids):
  return len(ids)

df_data = pd.DataFrame({'words': datas, 'tags': labels}, index=range(len(datas))) #DataFrame，索引是序列的个数，列是字序列以及对应的标签序列
df_data['length'] = df_data["tags"].apply(get_true_len) #获得每个序列真实的长度
df_data['length'][df_data['length'] > MAX_LEN] = MAX_LEN #这里需要注意，如果序列长度大于最大长度，则其真实长度必须设定为最大长度，否则后面会报错
df_data['x'] = df_data['words'].apply(X_padding) #超截短补，新定义一列
df_data['y'] = df_data['tags'].apply(y_padding) #超截短补，新定义一列
x = np.asarray(list(df_data['x'].values)) #转为list
y = np.asarray(list(df_data['y'].values)) #转为list
length = np.asarray(list(df_data['length'].values)) #转为list

#============================第四步：划分训练集、测试集、验证集==================================
#from sklearn.model_selection import train_test_split
#x_train,x_test, y_train, y_test = train_test_split(x, y, test_size=0.1, random_state=43) #random_state：避免每一个划分得不同
#x_train, x_valid, y_train, y_valid = train_test_split(x_train, y_train,  test_size=0.2, random_state=43)
#我们要加入每个序列的长度，因此sklearn自带的划分就没有用了，自己写一个
def split_data(data,label,seq_length,ratio):
  len_data = data.shape[0]
  #设置随机数种子，保证每次生成的结果都是一样的
  np.random.seed(43)
  #permutation随机生成0-len(data)随机序列
  shuffled_indices = np.random.permutation(len_data)
  #test_ratio为测试集所占的百分比
  test_set_size = int(len_data * ratio)
  test_indices = shuffled_indices[:test_set_size]
  train_indices = shuffled_indices[test_set_size:]
  train_data = data[train_indices,:]
  train_label = label[train_indices]
  train_seq_length = seq_length[train_indices]
  test_data = data[test_indices,:]
  test_label = label[test_indices]
  test_seq_length = seq_length[test_indices]
  return train_data,test_data,train_label,test_label,train_seq_length,test_seq_length
x_train,x_test, y_train, y_test, z_train, z_test = split_data(x, y, seq_length=length, ratio=0.1) #random_state：避免每一个划分得不同
x_train, x_valid, y_train, y_valid, z_train, z_valid = split_data(x_train, y_train, seq_length=z_train, ratio=0.2)

#============================第五步：将所有需要的存为pickle文件备用==================================
print('Finished creating the data generator.')
import pickle
import os
with open(os.path.join(PATH,'process_data/msra/MSRA.pkl'), 'wb') as outp:
  pickle.dump(word2id, outp)
  pickle.dump(id2word, outp)
  pickle.dump(tag2id, outp)
  pickle.dump(id2tag, outp)
  pickle.dump(x_train, outp)
  pickle.dump(y_train, outp)
  pickle.dump(z_train, outp)
  pickle.dump(x_test, outp)
  pickle.dump(y_test, outp)
  pickle.dump(z_test, outp)
  pickle.dump(x_valid, outp)
  pickle.dump(y_valid, outp)
  pickle.dump(z_valid, outp)
print('** Finished saving the data.')

中间步骤的df_data如下：

需要注意的是上面的训练、验证、测试数据都是从训练数据中切分的，不在字表中的字会用'unknow'的id进行映射，对于长度不够的句子会用0进行填充到最大长度。

3、定义模型

# -*- coding: utf-8 -*
import numpy as np
import tensorflow as tf

class BilstmCrfModel:
    def __init__(self,config,embedding_pretrained,dropout_keep=1):
      self.embedding_size = config.msraConfig.embedding_size
      self.embedding_dim = config.msraConfig.embedding_dim
      self.max_len = config.msraConfig.max_len
      self.tag_size = config.msraConfig.tag_size
      self.pretrained = config.msraConfig.pre_trained
      self.dropout_keep = dropout_keep
      self.embedding_pretrained = embedding_pretrained
      self.inputX = tf.placeholder(dtype=tf.int32, shape=[None,self.max_len], name="input_data")
      self.inputY = tf.placeholder(dtype=tf.int32,shape=[None,self.max_len], name="labels")
      self.seq_lens = tf.placeholder(dtype=tf.int32, shape=[None])
      self._build_net()

    def _build_net(self):
      # word_embeddings:[4027,100]
      # 词嵌入层
      with tf.name_scope("embedding"):
        # 利用预训练的词向量初始化词嵌入矩阵
        if self.pretrained:
            embedding_w = tf.Variable(tf.cast(self.embedding_pretrained, dtype=tf.float32, name="word2vec"),
                                      name="embedding_w")
        else:
            embedding_w = tf.get_variable("embedding_w", shape=[self.embedding_size, self.embedding_dim],
                                          initializer=tf.contrib.layers.xavier_initializer())
        # 利用词嵌入矩阵将输入的数据中的词转换成词向量，维度[batch_size, sequence_length, embedding_size]
        input_embedded = tf.nn.embedding_lookup(embedding_w, self.inputX)
        input_embedded = tf.nn.dropout(input_embedded,self.dropout_keep)

      with tf.name_scope("bilstm"):
        lstm_fw_cell = tf.nn.rnn_cell.LSTMCell(self.embedding_dim, forget_bias=1.0, state_is_tuple=True)
        lstm_bw_cell = tf.nn.rnn_cell.LSTMCell(self.embedding_dim, forget_bias=1.0, state_is_tuple=True)
        (output_fw, output_bw), states = tf.nn.bidirectional_dynamic_rnn(lstm_fw_cell,
                                          lstm_bw_cell,
                                          input_embedded,
                                          dtype=tf.float32,
                                          time_major=False,
                                          scope=None)
        bilstm_out = tf.concat([output_fw, output_bw], axis=2)

      # Fully connected layer.
      with tf.name_scope("output"):
        W = tf.get_variable(
            "output_w",
            shape=[2 * self.embedding_dim, self.tag_size],
            initializer=tf.contrib.layers.xavier_initializer())
        b = tf.Variable(tf.constant(0.1, shape=[self.max_len, self.tag_size]), name="output_b")
        self.bilstm_out = tf.tanh(tf.matmul(bilstm_out, W) + b)
      with tf.name_scope("crf"):
        # Linear-CRF.
        log_likelihood, self.transition_params = tf.contrib.crf.crf_log_likelihood(self.bilstm_out, self.inputY, self.seq_lens)

        self.loss = tf.reduce_mean(-log_likelihood)
        self.viterbi_sequence, viterbi_score = tf.contrib.crf.crf_decode(self.bilstm_out, self.transition_params, self.seq_lens)

4、定义主函数

from config.globalConfig import *
from config.msraConfig import Config
from dataset.msraDataset import MsraDataset
from utils.get_batch import BatchGenerator
from models.bilstm_crf import BilstmCrfModel
import tensorflow as tf
import os
import numpy as np
from utils.tmp import find_all_tag,get_labels,get_multi_metric,mean,get_binary_metric
labels_list = ['ns','nt','nr']

def train(config,model,save_path,trainBatchGen,valBatchGen):
  globalStep = tf.Variable(0, name="globalStep", trainable=False)
  save_path = os.path.join(save_path,"best_validation")
  saver = tf.train.Saver()
  with tf.Session() as sess:
    # 定义trainOp
    # 定义优化函数，传入学习速率参数
    optimizer = tf.train.AdamOptimizer(config.trainConfig.learning_rate)
    # 计算梯度,得到梯度和变量
    gradsAndVars = optimizer.compute_gradients(model.loss)
    # 将梯度应用到变量下，生成训练器
    trainOp = optimizer.apply_gradients(gradsAndVars, global_step=globalStep)
    sess.run(tf.global_variables_initializer())
    best_f_beta_val = 0.0 #最佳验证集的f1值
    for epoch in range(1,config.trainConfig.epoch+1):
      for trainX_batch,trainY_batch,train_seqlen in trainBatchGen.next_batch(config.trainConfig.batch_size):
        feed_dict = {
          model.inputX : trainX_batch, #[batch,max_len]
          model.inputY : trainY_batch, #[batch,max_len]
          model.seq_lens : train_seqlen, #[batch]
        }
        _, loss, pre = sess.run([trainOp,model.loss,model.viterbi_sequence],feed_dict)
        currentStep = tf.train.global_step(sess, globalStep)
        true_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(trainY_batch,train_seqlen)]
        pre_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(pre,train_seqlen)]
        precision,recall,f1 = get_multi_metric(true_idx2label,pre_idx2label,train_seqlen,labels_list)
        if currentStep % 100 == 0:
          print("[train] step:{} loss:{:.4f} precision:{:.4f} recall:{:.4f} f1:{:.4f}".format(currentStep,loss,precision,recall,f1))
        if currentStep % 100 == 0:
          #要计算所有验证样本的
          losses = []
          f_betas = []
          precisions = []
          recalls = []
          for valX_batch,valY_batch,val_seqlen in valBatchGen.next_batch(config.trainConfig.batch_size):
            feed_dict = {
              model.inputX : valX_batch, #[batch,max_len]
              model.inputY : valY_batch, #[batch,max_len]
              model.seq_lens : val_seqlen, #[batch]
            }
            val_loss, val_pre = sess.run([model.loss,model.viterbi_sequence],feed_dict)
            val_true_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(valY_batch,val_seqlen)]
            val_pre_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(val_pre,val_seqlen)]
            val_precision,val_recall,val_f1 = get_multi_metric(val_true_idx2label,val_pre_idx2label,val_seqlen,labels_list)
            losses.append(val_loss)
            f_betas.append(val_f1)
            precisions.append(val_precision)
            recalls.append(val_recall)
          if mean(f_betas) > best_f_beta_val:
            # 保存最好结果
            best_f_beta_val = mean(f_betas)
            last_improved = currentStep
            saver.save(sess=sess, save_path=save_path)
            improved_str = '*'
          else:
            improved_str = ''
          print("[val] loss:{:.4f} precision:{:.4f} recall:{:.4f} f1:{:.4f} {}".format(
            mean(losses),mean(precisions),mean(recalls),mean(f_betas),improved_str
          ))
def test(config,model,save_path,testBatchGen):
  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver = tf.train.Saver()
    ckpt = tf.train.get_checkpoint_state('checkpoint/msra/')
    path = ckpt.model_checkpoint_path
    saver.restore(sess, path)  # 读取保存的模型
    precisions = []
    recalls = []
    f1s = []
    for testX_batch,testY_batch,test_seqlen in testBatchGen.next_batch(config.trainConfig.batch_size):
      feed_dict = {
        model.inputX : testX_batch, #[batch,max_len]
        model.inputY : testY_batch, #[batch,max_len]
        model.seq_lens : test_seqlen, #[batch]
      }
      test_pre = sess.run([model.viterbi_sequence],feed_dict) #这里有点奇怪，和train、val出来的数据相比多了一个[]
      test_pre = test_pre[0]
      test_true_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(testY_batch,test_seqlen)]
      test_pre_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(test_pre,test_seqlen)]
      precision,recall,f1 = get_multi_metric(test_true_idx2label,test_pre_idx2label,test_seqlen,labels_list)
      precisions.append(precision)
      recalls.append(recall)
      f1s.append(f1)
    print("[test] precision:{:.4f} recall:{:.4f} f1:{:.4f}".format(
            mean(precisions),mean(recalls),mean(f1s)))

def predict(word2idx,idx2word,idx2label):
  max_len = 60
  input_list = []
  input_len = []
  with tf.Session() as sess:
    sess.run(tf.global_variables_initializer())
    saver = tf.train.Saver()
    ckpt = tf.train.get_checkpoint_state('checkpoint/msra/')
    path = ckpt.model_checkpoint_path
    saver.restore(sess, path)  # 读取保存的模型
    while True:
      print("请输入一句话：")
      line = input()
      if line == 'q':
        break
      line_len = len(line)
      input_len.append(line_len)
      word_list = [word2idx[word] if word in word2idx else word2idx['unknow'] for word in line]
      if line_len < max_len:
        word_list =word_list + [0]*(max_len-line_len)
      else:
        word_list = word_list[:max_len]
      input_list.append(word_list) #需要增加一个维度
      input_list = np.array(input_list)
      input_label = np.zeros((input_list.shape[0],input_list.shape[1])) #标签占位
      input_len = np.array(input_len)
      feed_dict = {
        model.inputX : input_list, #[batch,max_len]
        model.inputY : input_label, #[batch,max_len]
        model.seq_lens : input_len, #[batch]
      }
      pred_label = sess.run([model.viterbi_sequence],feed_dict)
      pred_label = pred_label[0]
      # 将预测标签id还原为真实标签
      pred_idx2label = [get_labels(label,idx2label,seq_len) for label,seq_len in zip(pred_label,input_len)]
      for line,pre,s_len in zip(input_list,pred_idx2label,input_len):
        res = find_all_tag(pre,s_len)
        for k in res:
          for v in res[k]:
            if v:
              print(k,"".join([idx2word[word] for word in line[v[0]:v[0]+v[1]]]))
      input_list = []
      input_len = []

if __name__ == "__main__":
  config = Config()
  msraDataset = MsraDataset(config)
  word2idx = msraDataset.get_word2idx()
  idx2word = msraDataset.get_idx2word()
  label2idx = msraDataset.get_label2idx()
  idx2label = msraDataset.get_idx2label()
  embedding_pre = msraDataset.get_embedding()
  x_train,y_train,z_train = msraDataset.get_train_data()
  x_val,y_val,z_val = msraDataset.get_val_data()
  x_test,y_test,z_test = msraDataset.get_test_data()
  print("====验证是否得到相关数据===")
  print("word2idx:",len(word2idx))
  print("idx2word:",len(idx2word))
  print("label2idx:",len(label2idx))
  print("idx2label:",len(idx2label))
  print("embedding_pre:",embedding_pre.shape)
  print(x_train.shape,y_train.shape,z_train.shape)
  print(x_val.shape,y_val.shape,z_val.shape)
  print(x_test.shape,y_test.shape,z_test.shape)
  print("======打印相关参数======")
  print("batch_size:",config.trainConfig.batch_size)
  print("learning_rate:",config.trainConfig.learning_rate)
  print("embedding_dim:",config.msraConfig.embedding_dim)
  is_train,is_val,is_test = True,True,True
  model = BilstmCrfModel(config,embedding_pre)
  if is_train:
    trainBatchGen = BatchGenerator(x_train,y_train,z_train,shuffle=True)
  if is_val:
    valBatchGen = BatchGenerator(x_val,y_val,z_val,shuffle=False)
  if is_test:
    testBatchGen = BatchGenerator(x_test,y_test,z_test,shuffle=False)
  dataset = "msra"
  if dataset == "msra":
    save_path = os.path.join(PATH,'checkpoint/msra/')
  if not os.path.exists(save_path):
    os.makedirs(save_path)
  #train(config,model,save_path,trainBatchGen,valBatchGen)
  #test(config,model,save_path,testBatchGen)
  predict(word2idx,idx2word,idx2label)

运行训练及测试：

部分结果：

====验证是否得到相关数据===
word2idx: 4026
idx2word: 4025
label2idx: 11
idx2label: 11
embedding_pre: (4027, 100)
(36066, 60) (36066, 60) (36066,)
(9016, 60) (9016, 60) (9016,)
(5009, 60) (5009, 60) (5009,)
======打印相关参数======
batch_size: 128
learning_rate: 0.001
embedding_dim: 100
。。。。。。
[train] step:10500 loss:0.8870 precision:0.9699 recall:0.9734 f1:1.0000
[val] loss:1.5881 precision:0.8432 recall:0.8569 f1:0.7964
[train] step:10600 loss:1.3130 precision:0.9445 recall:0.9314 f1:0.8696
[val] loss:1.6127 precision:0.8302 recall:0.8720 f1:0.7913
[train] step:10700 loss:0.9924 precision:0.9762 recall:0.9730 f1:0.9836
[val] loss:1.6147 precision:0.8490 recall:0.8488 f1:0.7923
[train] step:10800 loss:0.9863 precision:0.9481 recall:0.9495 f1:0.9697
[val] loss:1.6455 precision:0.8375 recall:0.8672 f1:0.8015
[train] step:10900 loss:1.0629 precision:0.9580 recall:0.9148 f1:0.9355
[val] loss:1.7692 precision:0.8174 recall:0.8064 f1:0.7735
[train] step:11000 loss:0.9846 precision:0.9800 recall:0.9689 f1:0.9643
[val] loss:1.5785 precision:0.8562 recall:0.8544 f1:0.7996
[train] step:11100 loss:0.9315 precision:0.9609 recall:0.9651 f1:0.9455
[val] loss:1.5665 precision:0.8568 recall:0.8646 f1:0.8100 *
[train] step:11200 loss:0.9989 precision:0.9804 recall:0.9629 f1:0.9697
[val] loss:1.5807 precision:0.8519 recall:0.8569 f1:0.7941 

[test] precision:0.8610 recall:0.8780 f1:0.8341

进行预测：

请输入一句话：
我要感谢洛杉矶市民议政论坛、亚洲协会南加中心、美中关系全国委员会、美中友协美西分会等友好团体的盛情款待。
2020-11-15 08:02:48.049927: I tensorflow/stream_executor/platform/default/dso_loader.cc:44] Successfully opened dynamic library libcublas.so.10
nt 洛杉矶市民议政论坛、亚洲协会南加中心、美中关系全国委员会、美中友协美西分会
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今天的演讲会是由哈佛大学费正清东亚研究中心主任傅高义主持的。
nt 哈佛大学
nt 清东亚研究中心
nr 傅高义
请输入一句话：
美方有哈佛大学典礼官亨特、美国驻华大使尚慕杰等。
nr 亨特、
nr 尚慕杰
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