【NLP】文本分类-情感分类_文本情感分类

1 常见NLP文本分类模型

1.1 TextCNN

        论文原文：《Convolutional Neural Networks for Sentence Classification》

        论文地址：1408.5882.pdf (arxiv.org)

        结构图如下：

          值得一提的是，在2016年的《A Sensitivity Analysis of (and Practitioners’ Guide to) Convolutional Neural Networks for Sentence Classification》作者通过大量实验对TextCNN进行网络参数选取，并给出了参数建议。论文地址：https://arxiv.org/pdf/1510.03820.pdf，文章经典的结构图如下：

 

 1.2 TextRNN

        TextRNN指的是利用RNN循环神经网络解决文本分类问题。

        论文原文：《Recurrent Neural Network for Text Classification with Multi-Task Learning》

        论文链接：https://www.ijcai.org/Proceedings/16/Papers/408.pdf

        结构图如下：

1.3 TextRCNN

        论文原文：《Recurrent Convolutional Neural Networks for Text Classification》

        论文链接：TextRCNN论文

        结构图如下：

1.4 FastText

        论文原文：《Bag of Tricks for Efficient Text Classification》

        论文链接：https://arxiv.org/pdf/1607.01759v2.pdf

        结构图如下：

1.5 HAN

        论文原文：《Hierarchical Attention Networks for Document Classification》

        论文链接：https://aclanthology.org/N16-1174.pdf

        结构图如下：

1.6 CharCNN

        论文原文：《Character-level Convolutional Networks for Text Classification》

        论文链接：CharCNN论文

        结构图如下：

1.7 Transformer

        论文原文：《Attention is all you need》

        论文链接：https://arxiv.org/pdf/1706.03762.pdf

        结构图如下：

2 代码实现

import torch
import torch.nn as nn
import torch.nn.functional as F
 
import numpy as np
 
import math
import copy
 
#TextCNN
class TextCNN(nn.Module):
    def __init__(self, args):
        super(TextCNN, self).__init__()
        self.args = args
 
        class_num = args.class_num
        chanel_num = 1
        filter_num = args.filter_num
 
        filter_sizes = args.filter_sizes
 
        vocabulary_size = args.vocabulary_size
        embedding_dimension = args.embedding_dim
        self.embedding = nn.Embedding(vocabulary_size, embedding_dimension)
        if args.static:
            self.embedding = self.embedding.from_pretrained(args.vectors, freeze=not args.non_static)
        if args.multichannel:
            self.embedding2 = nn.Embedding(vocabulary_size, embedding_dimension).from_pretrained(args.vectors)
            chanel_num += 1
        else:
            self.embedding2 = None
        self.convs = nn.ModuleList(
            [nn.Conv2d(chanel_num, filter_num, (size, embedding_dimension)) for size in filter_sizes])
        self.dropout = nn.Dropout(args.dropout)
        self.fc = nn.Linear(len(filter_sizes) * filter_num, class_num)
 
    def forward(self, x):
        if self.embedding2:
            x = torch.stack([self.embedding(x), self.embedding2(x)], dim=1)
        else:
            x = self.embedding(x)
            x = x.unsqueeze(1)
        x = [F.relu(conv(x)).squeeze(3) for conv in self.convs]
        x = [F.max_pool1d(item, int(item.size(2))).squeeze(2) for item in x]
        x = torch.cat(x, 1)
        x = self.dropout(x)
        logits = self.fc(x)
        return logits
 
#TextRNN
class LSTM(torch.nn.Module):
    def __init__(self, args):
        super(LSTM, self).__init__()
        self.embed_size = args.embedding_dim
        self.label_num = args.class_num
        self.embed_dropout = 0.1
        self.fc_dropout = 0.1
        self.hidden_num = 1
        self.hidden_size = 50
        self.hidden_dropout = 0
        self.bidirectional = True
 
        vocabulary_size = args.vocabulary_size
        embedding_dimension = args.embedding_dim
 
        self.embeddings = nn.Embedding(vocabulary_size, embedding_dimension)
        # self.embeddings.weight.data.copy_(torch.from_numpy(vocabulary_size))
 
        self.embeddings.weight.requires_grad = False
 
        self.lstm = nn.LSTM(
            self.embed_size,
            self.hidden_size,
            dropout=self.hidden_dropout,
            num_layers=self.hidden_num,
            batch_first=True,
            bidirectional=True
        )
        self.embed_dropout = nn.Dropout(self.embed_dropout)
        self.fc_dropout = nn.Dropout(self.fc_dropout)
        self.linear1 = nn.Linear(self.hidden_size * 2, self.label_num)
        self.softmax = nn.Softmax()
 
    def forward(self, input):
        x = self.embeddings(input)
        x = self.embed_dropout(x)
        batch_size = len(input)
        _, (lstm_out, _) = self.lstm(x)
        lstm_out = lstm_out.permute(1, 0, 2)
        lstm_out = lstm_out.contiguous().view(batch_size, -1)
        out = self.linear1(lstm_out)
        out = self.fc_dropout(out)
        out = self.softmax(out)
        return out
 
#TextRCNN
class BiLSTM(nn.Module):
    def __init__(self, args):
        super(BiLSTM, self).__init__()
        self.embed_size = args.embedding_dim
        self.label_num = args.class_num
        self.embed_dropout = 0.1
        self.fc_dropout = 0.1
        self.hidden_num = 2
        self.hidden_size = 50
        self.hidden_dropout = 0
        self.bidirectional = True
 
        vocabulary_size = args.vocabulary_size
        embedding_dimension = args.embedding_dim
 
        self.embeddings = nn.Embedding(vocabulary_size, embedding_dimension)
        # self.embeddings.weight.data.copy_(torch.from_numpy(word_embeddings))
        self.embeddings.weight.requires_grad = False
        self.lstm = nn.LSTM(
            self.embed_size,
            self.hidden_size,
            dropout=self.hidden_dropout,
            num_layers=self.hidden_num,
            batch_first=True,
            bidirectional=self.bidirectional
        )
        self.embed_dropout = nn.Dropout(self.embed_dropout)
        self.fc_dropout = nn.Dropout(self.fc_dropout)
        self.linear1 = nn.Linear(self.hidden_size * 2, self.hidden_size // 2)
        self.linear2 = nn.Linear(self.hidden_size // 2, self.label_num)
 
    def forward(self, input):
        out = self.embeddings(input)
        out = self.embed_dropout(out)
        out, _ = self.lstm(out)
        out = torch.transpose(out, 1, 2)
        out = torch.tanh(out)
        out = F.max_pool1d(out, out.size(2))
        out = out.squeeze(2)
        out = self.fc_dropout(out)
        out = self.linear1(F.relu(out))
        output = self.linear2(F.relu(out))
        return output
 
#FastText
class FastText(nn.Module):
    def __init__(self, args):
        super().__init__()
        self.output_dim = args.class_num
        vocabulary_size = args.vocabulary_size
        embedding_dimension = args.embedding_dim
        self.embeddings = nn.Embedding(vocabulary_size, embedding_dimension)
        self.fc = nn.Linear(embedding_dimension, self.output_dim)
 
    def forward(self, text):
        # text = [sent len, batch size]
        text = text.permute(1,0)
        embedded = self.embeddings(text)
        # embedded = [sent len, batch size, emb dim]
        embedded = embedded.permute(1, 0, 2)
        # embedded = [batch size, sent len, emb dim]
        pooled = F.avg_pool2d(embedded, (embedded.shape[1], 1)).squeeze(1)
        # pooled = [batch size, embedding_dim]
        return self.fc(pooled)
 
#HAN
class SelfAttention(nn.Module):
    def __init__(self, input_size, hidden_size):
        super(SelfAttention, self).__init__()
        self.W = nn.Linear(input_size, hidden_size, True)
        self.u = nn.Linear(hidden_size, 1)
    def forward(self, x):
        u = torch.tanh(self.W(x))
        a = F.softmax(self.u(u), dim=1)
        x = a.mul(x).sum(1)
        return x
class HAN(nn.Module):
    def __init__(self,args):
        super(HAN, self).__init__()
 
        hidden_size_gru = 50  # 50
        hidden_size_att = 100  # 100
 
        num_classes = args.class_num
 
        vocabulary_size = args.vocabulary_size
        embedding_dimension = args.embedding_dim
 
        self.num_words = 64 #词Pading大小
 
        self.embed = nn.Embedding(vocabulary_size, embedding_dimension)
        self.gru1 = nn.GRU(embedding_dimension, hidden_size_gru, bidirectional=True, batch_first=True)
        self.att1 = SelfAttention(hidden_size_gru * 2, hidden_size_att)
        self.gru2 = nn.GRU(hidden_size_att, hidden_size_gru, bidirectional=True, batch_first=True)
        self.att2 = SelfAttention(hidden_size_gru * 2, hidden_size_att)
        # 这里fc的参数很少，不需要dropout
        self.fc = nn.Linear(hidden_size_att, num_classes, True)
 
    def forward(self, x):
        # 64 512 200
        x = x.view(x.size(0) * self.num_words, -1).contiguous()
        x = self.embed(x)
        x, _ = self.gru1(x)
        x = self.att1(x)
        x = x.view(x.size(0) // self.num_words, self.num_words, -1).contiguous()
        x, _ = self.gru2(x)
        x = self.att2(x)
        x = self.fc(x)
        x = F.log_softmax(x, dim=1)  # softmax
        return x
 
#CharCNN
class CharCNN(nn.Module):
    def __init__(self, args):
        super(CharCNN, self).__init__()
 
        self.num_chars = 64
        self.features = [128, 128, 128, 128, 128, 128]
        self.kernel_sizes = [7, 7, 3, 3, 3, 3]
        self.dropout = args.dropout
        self.num_labels = args.class_num
 
        vocabulary_size = args.vocabulary_size
        embedding_dimension = args.embedding_dim
 
        # Embedding Layer
        self.embeddings = nn.Embedding(vocabulary_size, embedding_dimension)
        self.embeddings.weight.requires_grad = False
 
        self.in_features =  [self.num_chars]+self.features[:-1]
        self.out_features = self.features
 
        self.conv1d_1 = nn.Sequential(
            nn.Conv1d(self.in_features[0], self.out_features[0], self.kernel_sizes[0], stride=1),
            nn.BatchNorm1d(self.out_features[0]),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=3, stride=3)
        )
        self.conv1d_2 = nn.Sequential(
            nn.Conv1d(self.in_features[1], self.out_features[1], self.kernel_sizes[1], stride=1),
            nn.BatchNorm1d(self.out_features[1]),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=3, stride=3)
        )
        self.conv1d_3 = nn.Sequential(
            nn.Conv1d(self.in_features[2], self.out_features[2], self.kernel_sizes[2], stride=1),
            nn.BatchNorm1d(self.out_features[2]),
            nn.ReLU()
        )
        self.conv1d_4 = nn.Sequential(
            nn.Conv1d(self.in_features[3], self.out_features[3], self.kernel_sizes[3], stride=1),
            nn.BatchNorm1d(self.out_features[3]),
            nn.ReLU()
        )
        self.conv1d_5 = nn.Sequential(
            nn.Conv1d(self.in_features[4], self.out_features[4], self.kernel_sizes[4], stride=1),
            nn.BatchNorm1d(self.out_features[4]),
            nn.ReLU()
        )
        self.conv1d_6 = nn.Sequential(
            nn.Conv1d(self.in_features[5], self.out_features[5], self.kernel_sizes[5], stride=1),
            nn.BatchNorm1d(self.out_features[5]),
            nn.ReLU(),
            nn.MaxPool1d(kernel_size=3, stride=3)
        )
 
        self.fc1 = nn.Sequential(
            nn.Linear(128, 128),
            nn.ReLU(),
            nn.Dropout(self.dropout)
        )
        self.fc2 = nn.Sequential(
            nn.Linear(128, 128),
            nn.ReLU(),
            nn.Dropout(self.dropout)
        )
        self.fc3 = nn.Linear(128, self.num_labels)
 
    def forward(self, x):
        # x = torch.Tensor(x).long()   # batch_size=128, num_chars=128, seq_len=64
        x = self.embeddings(x)
        # x = x.permute(0,2,1)
        x = self.conv1d_1(x)  # b, out_features[0], （seq_len-f + 1）-f/s+1  = 64, 256, (1014-7+1)-3/3 + 1=1008-3/3+1=336
        x = self.conv1d_2(x)  # 64, 256, (336-7+1)-3/3+1=110
        x = self.conv1d_3(x)  # 64, 256, 110-3+1=108
        x = self.conv1d_4(x)  # 64, 256, 108-3+1=106
        x = self.conv1d_5(x)  # 64, 256, 106-3=1=104
        x = self.conv1d_6(x)  # 64, 256, (104-3+1)-3/3+1=34
 
        x = x.view(x.size(0), -1)  # 64, 256, 34 -> 64, 8704
        out = self.fc1(x)  # 64, 1024
        out = self.fc2(out)  # 64, 1024
        out = self.fc3(out)  # 64, 4
        return out
 
#Transformer
class Transformer_Config(object):
    """配置参数"""
    def __init__(self, args):
        # self.model_name = 'Transformer'
        # self.train_path = dataset + '/data/train.txt'                                # 训练集
        # self.dev_path = dataset + '/data/dev.txt'                                    # 验证集
        # self.test_path = dataset + '/data/test.txt'                                  # 测试集
        # self.class_list = [x.strip() for x in open(
        #     dataset + '/data/class.txt', encoding='utf-8').readlines()]              # 类别名单
        # self.vocab_path = dataset + '/data/vocab.pkl'                                # 词表
        # self.save_path = dataset + '/saved_dict/' + self.model_name + '.ckpt'        # 模型训练结果
        # self.log_path = dataset + '/log/' + self.model_name
        # self.embedding_pretrained = torch.tensor(
        #     np.load(dataset + '/data/' + embedding)["embeddings"].astype('float32'))\
        #     if embedding != 'random' else None                                       # 预训练词向量
        self.device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')   # 设备
 
        self.dropout = 0.5                                              # 随机失活
        self.require_improvement = 2000                                 # 若超过1000batch效果还没提升，则提前结束训练
        self.num_classes = args.class_num                         # 类别数
        self.n_vocab = args.vocabulary_size                                                # 词表大小，在运行时赋值
        self.num_epochs = args.epochs                                            # epoch数
        self.batch_size = args.batch_size                                           # mini-batch大小
        self.pad_size = 64                                              # 每句话处理成的长度(短填长切)
        self.learning_rate = 5e-4                                       # 学习率
        self.embedding_pretrained = None
        # self.embed = self.embedding_pretrained.size(1)\
        #     if self.embedding_pretrained is not None else 300           # 字向量维度
        self.embed = 128
        self.dim_model = args.embedding_dim
        self.hidden = 1024
        self.last_hidden = 512
        self.num_head = 2
        self.num_encoder = 2
'''Attention Is All You Need'''
class Transformer(nn.Module):
    def __init__(self, config):
        super(Transformer, self).__init__()
        if config.embedding_pretrained is not None:
            self.embedding = nn.Embedding.from_pretrained(config.embedding_pretrained, freeze=False)
        else:
            self.embedding = nn.Embedding(config.n_vocab, config.embed)
 
        self.postion_embedding = Positional_Encoding(config.embed, config.pad_size, config.dropout, config.device)
        self.encoder = Encoder(config.dim_model, config.num_head, config.hidden, config.dropout)
        self.encoders = nn.ModuleList([
            copy.deepcopy(self.encoder)
            # Encoder(config.dim_model, config.num_head, config.hidden, config.dropout)
            for _ in range(config.num_encoder)])
 
        self.fc1 = nn.Linear(config.pad_size * config.dim_model, config.num_classes)
        # self.fc2 = nn.Linear(config.last_hidden, config.num_classes)
        # self.fc1 = nn.Linear(config.dim_model, config.num_classes)
 
    def forward(self, x):
        out = self.embedding(x)
        out = self.postion_embedding(out)
        for encoder in self.encoders:
            out = encoder(out)
        out = out.view(out.size(0), -1)
        # out = torch.mean(out, 1)
        out = self.fc1(out)
        return out
class Encoder(nn.Module):
    def __init__(self, dim_model, num_head, hidden, dropout):
        super(Encoder, self).__init__()
        self.attention = Multi_Head_Attention(dim_model, num_head, dropout)
        self.feed_forward = Position_wise_Feed_Forward(dim_model, hidden, dropout)
 
    def forward(self, x):
        out = self.attention(x)
        out = self.feed_forward(out)
        return out
class Positional_Encoding(nn.Module):
    def __init__(self, embed, pad_size, dropout, device):
        super(Positional_Encoding, self).__init__()
        self.device = device
        self.pe = torch.tensor([[pos / (10000.0 ** (i // 2 * 2.0 / embed)) for i in range(embed)] for pos in range(pad_size)])
        self.pe[:, 0::2] = np.sin(self.pe[:, 0::2])
        self.pe[:, 1::2] = np.cos(self.pe[:, 1::2])
        self.dropout = nn.Dropout(dropout)
 
    def forward(self, x):
        out = x + nn.Parameter(self.pe, requires_grad=False).to(self.device)
        out = self.dropout(out)
        return out
class Scaled_Dot_Product_Attention(nn.Module):
    '''Scaled Dot-Product Attention '''
    def __init__(self):
        super(Scaled_Dot_Product_Attention, self).__init__()
 
    def forward(self, Q, K, V, scale=None):
        '''
        Args:
            Q: [batch_size, len_Q, dim_Q]
            K: [batch_size, len_K, dim_K]
            V: [batch_size, len_V, dim_V]
            scale: 缩放因子 论文为根号dim_K
        Return:
            self-attention后的张量，以及attention张量
        '''
        attention = torch.matmul(Q, K.permute(0, 2, 1))
        if scale:
            attention = attention * scale
        # if mask:  # TODO change this
        #     attention = attention.masked_fill_(mask == 0, -1e9)
        attention = F.softmax(attention, dim=-1)
        context = torch.matmul(attention, V)
        return context
class Multi_Head_Attention(nn.Module):
    def __init__(self, dim_model, num_head, dropout=0.0):
        super(Multi_Head_Attention, self).__init__()
        self.num_head = num_head
        assert dim_model % num_head == 0
        self.dim_head = dim_model // self.num_head
        self.fc_Q = nn.Linear(dim_model, num_head * self.dim_head)
        self.fc_K = nn.Linear(dim_model, num_head * self.dim_head)
        self.fc_V = nn.Linear(dim_model, num_head * self.dim_head)
        self.attention = Scaled_Dot_Product_Attention()
        self.fc = nn.Linear(num_head * self.dim_head, dim_model)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(dim_model)
 
    def forward(self, x):
        batch_size = x.size(0)
        Q = self.fc_Q(x)
        K = self.fc_K(x)
        V = self.fc_V(x)
        Q = Q.view(batch_size * self.num_head, -1, self.dim_head)
        K = K.view(batch_size * self.num_head, -1, self.dim_head)
        V = V.view(batch_size * self.num_head, -1, self.dim_head)
        # if mask:  # TODO
        #     mask = mask.repeat(self.num_head, 1, 1)  # TODO change this
        scale = K.size(-1) ** -0.5  # 缩放因子
        context = self.attention(Q, K, V, scale)
 
        context = context.view(batch_size, -1, self.dim_head * self.num_head)
        out = self.fc(context)
        out = self.dropout(out)
        out = out + x  # 残差连接
        out = self.layer_norm(out)
        return out
class Position_wise_Feed_Forward(nn.Module):
    def __init__(self, dim_model, hidden, dropout=0.0):
        super(Position_wise_Feed_Forward, self).__init__()
        self.fc1 = nn.Linear(dim_model, hidden)
        self.fc2 = nn.Linear(hidden, dim_model)
        self.dropout = nn.Dropout(dropout)
        self.layer_norm = nn.LayerNorm(dim_model)
 
    def forward(self, x):
        out = self.fc1(x)
        out = F.relu(out)
        out = self.fc2(out)
        out = self.dropout(out)
        out = out + x  # 残差连接
        out = self.layer_norm(out)
        return out

3 结果讨论

        本文针对文本情感二分类任务展开训练，采用数据集的数据量包含Train有56700条，Evaluate有7000条。得到测试结果如下表。

        可以看到由于本文的数据量比较小，所以小模型还有更好的检测效果。如Transformer有点大材小用，缺少发挥空间。欢迎大家学习讨论。