对话系统——基于神经网络的NER命名实体识别

概述

项目地址：https://github.com/audier/my_nlp_play

• 该项目整理deeppavlov中代码而成，参考该框架的实现方法搭建一个命名实体的识别系统。
• 该项目使用了conll2003_v2数据集，其中标注的命名实体共计九类：

['O', 'B-LOC', 'B-PER', 'B-ORG', 'I-PER', 'I-ORG', 'B-MISC', 'I-LOC', 'I-MISC']

• 实现了将输入识别为命名实体的模型，如下所示：

# input
['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.']
# output
['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O']

模型实现

数据下载并解压

导入下载和解压所需插件，下载训练数据并解压：

import os

if not os.path.exists('data/train.txt'):
	# 导入下载和解压所需插件
	import wget, tarfile
	DATA_URL = 'http://files.deeppavlov.ai/deeppavlov_data/conll2003_v2.tar.gz'
	out_fname = 'data.tar.gz'
	wget.download(DATA_URL, out=out_fname)
	tar = tarfile.open(out_fname)
	tar.extractall('data/')
	tar.close()
	os.remove(out_fname)

训练数据格式如下：

-DOCSTART- -X- -X- O

EU		NNP B-NP B-ORG
rejects VBZ B-VP O
German 	JJ 	B-NP B-MISC
call 	NN 	I-NP O
to 		TO 	B-VP O
boycott VB 	I-VP O
British JJ 	B-NP B-MISC
lamb 	NN 	I-NP O
. 		. 	O 	 O

读取数据库

将下载好的数据读取到内存中去，我们只需要每行开头和最后一个数据，他们分别是文本信息和命名实体。

我们读取后的数据data格式为：

    {'train': [(['Mr.', 'Dwag', 'are', 'derping', 'around'], ['B-PER', 'I-PER', 'O', 'O', 'O']), ....],
     'valid': [...],
     'test': [...]}

通过data['train'][0]就可以得到所需数据，方便训练：

(['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], 
['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O'])

所需代码如下：

class NerDatasetReader:
    def read(self, data_path):
        data_parts = ['train', 'valid', 'test']
        extension = '.txt'
        dataset = {}
        for data_part in data_parts:
            file_path = data_path + data_part + extension
            dataset[data_part] = self.read_file(str(file_path))
        return dataset
            
    def read_file(self, file_path):
        fileobj = open(file_path, 'r', encoding='utf-8')
        samples = []
        tokens = []
        tags = []
        for content in fileobj:
            content = content.strip('\n')
            if content == '-DOCSTART- -X- -X- O':
                pass
            elif content == '':
                if len(tokens) != 0:
                    samples.append((tokens, tags))
                    tokens = []
                    tags = []
            else:
                tokens.append(content.split(' ', 1)[0])
                tags.append(content.split(' ')[-1])
        return samples

测试一下：

# 读取下载好的数据
dataset_reader = NerDatasetReader()
dataset = dataset_reader.read('data/')

for sample in dataset['train'][:4]:
    for token, tag in zip(*sample):
        print('%s\t%s' % (token, tag))
    print()

提取每行第一个和最后一个数据，测试结果：

EU			B-ORG
rejects		O
German		B-MISC
call		O
to			O
boycott		O
British		B-MISC
lamb		O
.			O

准备字典

文本信息和命名实体不能真正的应用到神经网络中，所以需要一个字典将文本信息和命名实体映射为模型能够接受的数字标签。

from collections import defaultdict, Counter
from itertools import chain
import numpy as np

class Vocab:
    def __init__(self,
                 special_tokens=tuple()):
        self.special_tokens = special_tokens
        self._t2i = defaultdict(lambda: 1)
        self._i2t = []
        
    def fit(self, tokens):
        count = 0
        self.freqs = Counter(chain(*tokens))
        # The first special token will be the default token
        for special_token in self.special_tokens:
            self._t2i[special_token] = count
            self._i2t.append(special_token)
            count += 1
        for token, freq in self.freqs.most_common():
            if token not in self._t2i:
                # t2i是字典，i2t是所有字符串的列表
                self._t2i[token] = count
                self._i2t.append(token)
                count += 1

    def __call__(self, batch, **kwargs):
        # Implement the vocab() method. The input could be a batch of tokens
        # or a batch of indices. A batch is a list of utterances where each
        # utterance is a list of tokens
        indices_batch = []
        for tokens in batch:
            indices = []
            for token in tokens:
                indices.append(self[token])
            indices_batch.append(indices)
        return indices_batch

    def __getitem__(self, key):
        # Implement the vocab[] method. The input could be a token
        # (string) or an index. You have to detect what type of data
        # is key and return. 
        if isinstance(key, (int, np.integer)):
            return self._i2t[key]
        elif isinstance(key, str):
            return self._t2i[key]
        else:
            raise NotImplementedError("not implemented for type `{}`".format(type(key)))
    
    def __len__(self):
        return len(self._i2t)

可以通过一个小代码测试一下：

# 例子：
# 增加特殊符号
special_tokens = ['<UNK>']
# 实例化
token_vocab = Vocab(special_tokens)
tag_vocab = Vocab()
# 用于进行训练的数据
all_tokens_by_sentenses = [tokens for tokens, tags in dataset['train']]
all_tags_by_sentenses = [tags for tokens, tags in dataset['train']]
# 进行训练
token_vocab.fit(all_tokens_by_sentenses)
tag_vocab.fit(all_tags_by_sentenses)

print(token_vocab([['be','to']]))
print(token_vocab([[39,6]]))
print(tag_vocab([[0,1,2,3,4,5,6,7,8]]))

得到结果如下：

[[39, 6]]
[['be', 'to']]
[['O', 'B-LOC', 'B-PER', 'B-ORG', 'I-PER', 'I-ORG', 'B-MISC', 'I-LOC', 'I-MISC']]

建立数据生成器

建立数据生成器是我们在用TensorFlow进行训练时常用的一种方法，如果把所有的数据一股脑放到内存里会很占用空间，用数据生成器的方法会在你使用的过程中生成数据，降低不必要的内存占用（这个是我个人理解）。

class DatasetIterator:
    def __init__(self, data):
        self.data = {
            'train': data['train'],
            'valid': data['valid'],
            'test': data['test']
        }

    def gen_batches(self, batch_size, data_type='train', shuffle=True):
        n = 0
        while n < 10:
            x = []
            y = []
            for i in range(batch_size):
                num = n * batch_size + i
                datas = self.data[data_type]
                x.append(datas[num][0])
                y.append(datas[num][1])
            n = n + 1
            yield x, y

测试：

data_iterator = DatasetIterator(dataset)
x, y = next(data_iterator.gen_batches(2))
print(x,'\n',y)

结果：

[['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], ['Peter', 'Blackburn']] 
 [['B-ORG', 'O', 'B-MISC', 'O', 'O', 'O', 'B-MISC', 'O', 'O'], ['B-PER', 'I-PER']]

Mask的使用

为了方便训练，常常需要同一个bantch的数据padding到相同大小进行训练，但是多余的数据会影响损失。因此需要mask对损失进行修正。mask提供功能如下：

#input:
[['EU', 'rejects', 'German', 'call', 'to', 'boycott', 'British', 'lamb', '.'], ['Peter', 'Blackburn']]
#output:
[[1. 1. 1. 1. 1.], [1. 1. 0. 0. 0.]]

训练的时候第二个数据会补零到第一个数据的长度，但是计算损失的时候补零的损失会产生一定影响，因此mask相当于一个滤波器，将额外的损失过滤掉，实现方法如下：

class Mask():
	"""docstring for Mask"""
	def __init__(self):
		pass

	def __call__(self, token_batches, **kwargs):
		batches_size = len(token_batches)
		maxlen = max(len(utt) for utt in token_batches)
		mask = np.zeros([batches_size, maxlen], dtype=np.float32)
		for n, utt in enumerate(token_batches):
			mask[n, :len(utt)] = 1
		return mask

测试代码：

get_mask = Mask()
print(get_mask([['Try', 'to', 'get', 'the', 'mask'], ['Check', 'paddings']]))

测试结果：

[[1. 1. 1. 1. 1.], [1. 1. 0. 0. 0.]]

搭建卷积神经网络

import tensorflow as tf

# 词嵌入层，输入形如[batch_size, num_tokens]的数据，输出词向量信息会通过tokenindex值找到词向量
def get_embeddings(indices, vocabulary_size, emb_dim):
    # Initialize the random gaussian matrix with dimensions [vocabulary_size, embedding_dimension]
    # The **VARIANCE** of the random samples must be 1 / embedding_dimension
    emb_mat = np.random.randn(vocabulary_size, emb_dim).astype(np.float32) / np.sqrt(emb_dim)
    emb_mat = tf.Variable(emb_mat, name='Embeddings', trainable=True)
    emb = tf.nn.embedding_lookup(emb_mat, indices)
    return emb


# 卷积层
# units为输入形状，n_hidden_list为隐藏层卷积核个数，cnn_fileter_width为卷积核大小, activ激活函数
def conv_net(units, n_hidden_list, cnn_filter_width, activation=tf.nn.relu):
    # Use activation(units) to apply activation to units
    for n_hidden in n_hidden_list:
        
        units = tf.layers.conv1d(units,
                                 n_hidden,
                                 cnn_filter_width,
                                 padding='same')
        units = activation(units)
    return units


# 损失函数
# 
def masked_cross_entropy(logits, label_indices, number_of_tags, mask):
    ground_truth_labels = tf.one_hot(label_indices, depth=number_of_tags)
    loss_tensor = tf.nn.softmax_cross_entropy_with_logits_v2(labels=ground_truth_labels, logits=logits)
    loss_tensor *= mask
    loss = tf.reduce_mean(loss_tensor)
    return loss


# 创建完整的神经网络
class NerNetwork:
    def __init__(self,
                 n_tokens,
                 n_tags,
                 token_emb_dim=100,
                 n_hidden_list=(128,),
                 cnn_filter_width=7,
                 use_batch_norm=False,
                 embeddings_dropout=False,
                 top_dropout=False,
                 **kwargs):
        
        # ================ Building inputs =================
        
        self.learning_rate_ph = tf.placeholder(tf.float32, [])
        self.dropout_keep_ph = tf.placeholder(tf.float32, [])
        self.token_ph = tf.placeholder(tf.int32, [None, None], name='token_ind_ph')
        self.mask_ph = tf.placeholder(tf.float32, [None, None], name='Mask_ph')
        self.y_ph = tf.placeholder(tf.int32, [None, None], name='y_ph')
        
        # ================== Building the network ==================
        
        # Now embedd the indices of tokens using token_emb_dim function
        emb = get_embeddings(self.token_ph, n_tokens, token_emb_dim)
        emb = tf.nn.dropout(emb, self.dropout_keep_ph, (tf.shape(emb)[0], 1, tf.shape(emb)[2]))
        
        # Build a multilayer CNN on top of the embeddings.
        # The number of units in the each layer must match
        # corresponding number from n_hidden_list.
        # Use ReLU activation 
        units = conv_net(emb, n_hidden_list, cnn_filter_width)
        units = tf.nn.dropout(units, self.dropout_keep_ph, (tf.shape(units)[0], 1, tf.shape(units)[2]))
        logits = tf.layers.dense(units, n_tags, activation=None)
        self.predictions = tf.argmax(logits, 2)
        
        # ================= Loss and train ops =================
        # Use cross-entropy loss. check the tf.nn.softmax_cross_entropy_with_logits_v2 function
        self.loss = masked_cross_entropy(logits, self.y_ph, n_tags, self.mask_ph)

        # Create a training operation to update the network parameters.
        # We purpose to use the Adam optimizer as it work fine for the
        # most of the cases. Check tf.train to find an implementation.
        # Put the train operation to the attribute self.train_op
        
        optimizer = tf.train.AdamOptimizer(self.learning_rate_ph)
        self.train_op = optimizer.minimize(self.loss)

        # ================= Initialize the session =================
        
        self.sess = tf.Session()
        self.sess.run(tf.global_variables_initializer())

    def __call__(self, tok_batch, mask_batch):
        feed_dict = {self.token_ph: tok_batch,
                     self.mask_ph: mask_batch,
                     self.dropout_keep_ph: 1.0}
        return self.sess.run(self.predictions, feed_dict)

    def train_on_batch(self, tok_batch, tag_batch, mask_batch, dropout_keep_prob, learning_rate):
        feed_dict = {self.token_ph: tok_batch,
                     self.y_ph: tag_batch,
                     self.mask_ph: mask_batch,
                     self.dropout_keep_ph: dropout_keep_prob,
                     self.learning_rate_ph: learning_rate}
        self.sess.run(self.train_op, feed_dict)


nernet = NerNetwork(len(token_vocab),
                    len(tag_vocab),
                    n_hidden_list=[100, 100])

padding

# zero pad 顾名思义
# zero_pad takes a batch of lists of token indices, pad it with zeros to the
# maximal length and convert it to numpy matrix
def zero_pad(batch, dtype=np.float32):
    if len(batch) == 1 and len(batch[0]) == 0:
        return np.array([], dtype=dtype)
    batch_size = len(batch)
    max_len = max(len(utterance) for utterance in batch)
    if isinstance(batch[0][0], (int, np.int)):
        padded_batch = np.zeros([batch_size, max_len], dtype=np.int32)
        for n, utterance in enumerate(batch):
            padded_batch[n, :len(utterance)] = utterance
    else:
        n_features = len(batch[0][0])
        padded_batch = np.zeros([batch_size, max_len, n_features], dtype=dtype)
        for n, utterance in enumerate(batch):
            for k, token_features in enumerate(utterance):
                padded_batch[n, k] = token_features
    return padded_batch

执行训练

batch_size = 64 # YOUR HYPERPARAMETER HERE
n_epochs = 80 # YOUR HYPERPARAMETER HERE
learning_rate = 0.001 # YOUR HYPERPARAMETER HERE
dropout_keep_prob = 0.5 # YOUR HYPERPARAMETER HERE



for epoch in range(n_epochs):
    for x, y in data_iterator.gen_batches(batch_size, 'train'):
        # Convert tokens to indices via Vocab
        x_inds = token_vocab(x) # YOUR CODE 
        # Convert tags to indices via Vocab
        y_inds = tag_vocab(y) # YOUR CODE 
        
        # Pad every sample with zeros to the maximal length
        x_batch = zero_pad(x_inds)
        y_batch = zero_pad(y_inds)

        mask = get_mask(x)
        nernet.train_on_batch(x_batch, y_batch, mask, dropout_keep_prob, learning_rate)



# 测试结果
sentence = 'EU rejects German call to boycott British lamb .'
x = [sentence.split()]

x_inds = token_vocab(x)
x_batch = zero_pad(x_inds)
mask = get_mask(x)
y_inds = nernet(x_batch, mask)
print(x[0])
print(tag_vocab(y_inds)[0])

转载请注明出处：hongwen