A Neural Probabilistic Language Model

《A Neural Probabilistic Language Model》，这篇论文第一次用神经网络解决语言模型的问题，比传统的语言模型使用n-gram建模更远的关系，且考虑词与词之间的相似性。
论文中采用多层感知器（MLP)构造了语言模型，如下图所示，模型共分为三层，即映射层、隐藏层、输出层，利用前n个单词得到下一个单词。具体的代码如下所示。

import copy
import torch
import torch.nn as nn
import torch.optim as optim

#根据论文公式构建模型
class NNLM(nn.Module):
    def __init__(self, dict_size, edim, input_dim, hidden_dim):
        super(NNLM, self).__init__()
        self.input_dim = input_dim
        self.C = nn.Embedding(dict_size, edim)  
        self.H = nn.Linear(input_dim, hidden_dim, bias=False)
        self.d = nn.Parameter(torch.ones(hidden_dim))
        self.U = nn.Linear(hidden_dim, dict_size, bias=False)
        self.W = nn.Linear(input_dim, dict_size, bias=False)
        self.b = nn.Parameter(torch.ones(dict_size))

    def forward(self, X):
        X = self.C(X) #映射层 (batch_size,n_gram,edim)  n_gram是指词的个数
        X = X.view(-1, self.input_dim)  #(batch_size,input_dim)
        tanh = torch.tanh(self.d + self.H(X)) #隐藏层(batch_size,hidden_dim)
        output = self.b + self.W(X) + self.U(tanh) #输出层(batch_size,dict_size)
        return output


#将文本集合转化成input，target的形式
def make_batch(sentences,n_gram,word_dict):
    input_batch = []
    target_batch = []
    for sentence in sentences:
        words = sentence.split()  #分词
        if n_gram+1 > len(words):
            raise Exception('n_gram大于文本长度')
        for i in range(len(words)-n_gram):
            input = [word_dict[n] for n in words[i:i+n_gram]]
            target = word_dict[words[i+n_gram]]

            input_batch.append(input)
            target_batch.append(target)
    return input_batch, target_batch

if __name__=='__main__':
    sentences = ["i like dog", "i love coffee", "i hate milk"]
    word_list = " ".join(sentences).split()
    word_list = list(set(word_list))
    word_dict = {w: i for i, w in enumerate(word_list)}
    number_dict = {i: w for i, w in enumerate(word_list)}
    dict_size = len(word_dict)  # 字典的大小
    edim = 3  #维度大小
    n_gram = 2  #设置窗口大小
    input_dim = edim*n_gram  
    hidden_dim = 2  #隐向量大小

    model = NNLM(dict_size, edim, input_dim, hidden_dim)  #加载模型
    criterion = nn.CrossEntropyLoss()  #损失
    optimizer = optim.Adam(model.parameters(), lr=0.001)   #优化器
    input_batch, target_batch = make_batch(sentences, n_gram, word_dict)
    input_text = copy.deepcopy(input_batch)
    input_batch = torch.LongTensor(input_batch)  #类型转换
    target_batch = torch.LongTensor(target_batch)

    # 模型训练
    for epoch in range(1,10001):
        optimizer.zero_grad()   #梯度清零
        output = model(input_batch)
        loss = criterion(output, target_batch)
        if epoch % 1000 == 0:
            print('Epoch:', '%04d' % (epoch), 'cost =', '{:.6f}'.format(loss))
        loss.backward()  #反向传播，计算当前梯度
        optimizer.step()  #根据梯度更新网络参数

    # 模型预测
    predict = model(input_batch).data.max(1, keepdim=True)[1]
    print("真实标签：",[number_dict[n.item()] for n in target_batch],'->','预测标签：',[number_dict[n.item()] for n in predict.squeeze()])

NNLM模型使用低维稠密词向量对上文进行表示，解决了词袋模型带来的数据稀疏、语义鸿沟等问题。NNLM可以根据上下文语境预测下一个目标词，优于传统的n-gram的模型。