采用多种深度学习、机器学习算法实现目标意图识别系统——含完整项目源码

基于Python多种深度学习、机器学习算法的目标意图识别系统

引言

目标意图识别是自然语言处理中的一个重要任务，广泛应用于智能客服、语音助手等领域。本文将介绍如何使用Python实现多种深度学习和机器学习算法来构建目标意图识别系统。我们将使用两个英文数据集ATIS和SNIPS，并分别使用SVM、LR、Stack-Propagation、Bi-model with decoder、Bi-LSTM、JointBERT和ERNIE等算法进行训练和测试。

🚀完整项目源码下载链接👉：https://download.csdn.net/download/DeepLearning_/89938355

数据集介绍

1. ATIS 数据集

• 描述：航空旅行信息系统的英文数据集。
• 训练数据：4978条
• 测试数据：888条
• 类别：22个

2. SNIPS 数据集

• 描述：智能个人助手的英文数据集。
• 训练数据：13784条
• 测试数据：700条
• 类别：7个

算法介绍

1. SVM（支持向量机）

支持向量机是一种监督学习模型，用于分类和回归分析。它通过找到一个超平面来最大化不同类别之间的间隔。

2. LR（逻辑回归）

逻辑回归是一种广义线性模型，用于二分类或多分类问题。它通过sigmoid函数将线性组合的结果映射到0和1之间。

3. Stack-Propagation（堆叠传播）

堆叠传播是一种深度学习方法，通过多层神经网络逐步学习数据的高级特征。

4. Bi-model with decoder（双向模型加解码器）

双向模型结合了前向和后向的信息，解码器则用于生成最终的输出。

5. Bi-LSTM（双向长短期记忆网络）

双向LSTM通过前向和后向两个方向的LSTM单元来捕捉序列数据的上下文信息。

6. JointBERT

JointBERT是一种基于BERT的联合意图识别和槽位填充模型，通过预训练的BERT模型进行迁移学习。

7. ERNIE

ERNIE是百度提出的一种增强版的BERT模型，通过引入知识图谱等外部知识来提升模型性能。

环境搭建

确保安装了以下软件和库：

• Python 3.x
• PyTorch
• Transformers
• Scikit-learn
• Pandas
• Numpy

安装所需的库：

pip install torch transformers scikit-learn pandas numpy

算法实现

1. SVM 实现（仅供参考）

# train.py
import argparse
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.svm import SVC
from sklearn.metrics import classification_report

def load_data(dataset):
    if dataset == 'atis':
        train_data = pd.read_csv('data/atis_train.csv')
        test_data = pd.read_csv('data/atis_test.csv')
    elif dataset == 'snips':
        train_data = pd.read_csv('data/snips_train.csv')
        test_data = pd.read_csv('data/snips_test.csv')
    return train_data, test_data

def main(args):
    train_data, test_data = load_data(args.dataset)
    
    vectorizer = TfidfVectorizer()
    X_train = vectorizer.fit_transform(train_data['text'])
    X_test = vectorizer.transform(test_data['text'])
    
    y_train = train_data['intent']
    y_test = test_data['intent']
    
    model = SVC()
    model.fit(X_train, y_train)
    
    y_pred = model.predict(X_test)
    print(classification_report(y_test, y_pred))

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--dataset', type=str, default='atis', help='Dataset to use (atis or snips)')
    args = parser.parse_args()
    main(args)

2. LR 实现（仅供参考）

# train.py
import argparse
import pandas as pd
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.linear_model import LogisticRegression
from sklearn.metrics import classification_report

def load_data(dataset):
    if dataset == 'atis':
        train_data = pd.read_csv('data/atis_train.csv')
        test_data = pd.read_csv('data/atis_test.csv')
    elif dataset == 'snips':
        train_data = pd.read_csv('data/snips_train.csv')
        test_data = pd.read_csv('data/snips_test.csv')
    return train_data, test_data

def main(args):
    train_data, test_data = load_data(args.dataset)
    
    vectorizer = TfidfVectorizer()
    X_train = vectorizer.fit_transform(train_data['text'])
    X_test = vectorizer.transform(test_data['text'])
    
    y_train = train_data['intent']
    y_test = test_data['intent']
    
    model = LogisticRegression()
    model.fit(X_train, y_train)
    
    y_pred = model.predict(X_test)
    print(classification_report(y_test, y_pred))

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--dataset', type=str, default='atis', help='Dataset to use (atis or snips)')
    args = parser.parse_args()
    main(args)

3. Stack-Propagation 实现（仅供参考）

# train.py
import argparse
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report

class StackPropagation(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(StackPropagation, self).__init__()
        self.fc1 = nn.Linear(input_dim, hidden_dim)
        self.fc2 = nn.Linear(hidden_dim, hidden_dim)
        self.fc3 = nn.Linear(hidden_dim, output_dim)
        self.relu = nn.ReLU()

    def forward(self, x):
        out = self.fc1(x)
        out = self.relu(out)
        out = self.fc2(out)
        out = self.relu(out)
        out = self.fc3(out)
        return out

def load_data(dataset):
    if dataset == 'atis':
        train_data = pd.read_csv('data/atis_train.csv')
        test_data = pd.read_csv('data/atis_test.csv')
    elif dataset == 'snips':
        train_data = pd.read_csv('data/snips_train.csv')
        test_data = pd.read_csv('data/snips_test.csv')
    return train_data, test_data

def main(args):
    train_data, test_data = load_data(args.dataset)
    
    vectorizer = TfidfVectorizer()
    X_train = vectorizer.fit_transform(train_data['text']).toarray()
    X_test = vectorizer.transform(test_data['text']).toarray()
    
    y_train = train_data['intent'].values
    y_test = test_data['intent'].values
    
    input_dim = X_train.shape[1]
    hidden_dim = 128
    output_dim = len(set(y_train))
    
    model = StackPropagation(input_dim, hidden_dim, output_dim)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    
    X_train = torch.tensor(X_train, dtype=torch.float32)
    y_train = torch.tensor(y_train, dtype=torch.long)
    X_test = torch.tensor(X_test, dtype=torch.float32)
    y_test = torch.tensor(y_test, dtype=torch.long)
    
    for epoch in range(100):
        optimizer.zero_grad()
        outputs = model(X_train)
        loss = criterion(outputs, y_train)
        loss.backward()
        optimizer.step()
    
    with torch.no_grad():
        outputs = model(X_test)
        _, predicted = torch.max(outputs, 1)
        print(classification_report(y_test.numpy(), predicted.numpy()))

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--dataset', type=str, default='atis', help='Dataset to use (atis or snips)')
    args = parser.parse_args()
    main(args)

4. Bi-model with decoder 实现（仅供参考）

# train.py
import argparse
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report

class BiModelWithDecoder(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(BiModelWithDecoder, self).__init__()
        self.encoder = nn.LSTM(input_dim, hidden_dim, bidirectional=True, batch_first=True)
        self.decoder = nn.LSTM(hidden_dim * 2, hidden_dim, batch_first=True)
        self.fc = nn.Linear(hidden_dim, output_dim)
        self.relu = nn.ReLU()

    def forward(self, x):
        encoded, _ = self.encoder(x)
        decoded, _ = self.decoder(encoded)
        out = self.fc(decoded[:, -1, :])
        return out

def load_data(dataset):
    if dataset == 'atis':
        train_data = pd.read_csv('data/atis_train.csv')
        test_data = pd.read_csv('data/atis_test.csv')
    elif dataset == 'snips':
        train_data = pd.read_csv('data/snips_train.csv')
        test_data = pd.read_csv('data/snips_test.csv')
    return train_data, test_data

def main(args):
    train_data, test_data = load_data(args.dataset)
    
    vectorizer = TfidfVectorizer()
    X_train = vectorizer.fit_transform(train_data['text']).toarray()
    X_test = vectorizer.transform(test_data['text']).toarray()
    
    y_train = train_data['intent'].values
    y_test = test_data['intent'].values
    
    input_dim = X_train.shape[1]
    hidden_dim = 128
    output_dim = len(set(y_train))
    
    model = BiModelWithDecoder(input_dim, hidden_dim, output_dim)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    
    X_train = torch.tensor(X_train, dtype=torch.float32).unsqueeze(1)
    y_train = torch.tensor(y_train, dtype=torch.long)
    X_test = torch.tensor(X_test, dtype=torch.float32).unsqueeze(1)
    y_test = torch.tensor(y_test, dtype=torch.long)
    
    for epoch in range(100):
        optimizer.zero_grad()
        outputs = model(X_train)
        loss = criterion(outputs, y_train)
        loss.backward()
        optimizer.step()
    
    with torch.no_grad():
        outputs = model(X_test)
        _, predicted = torch.max(outputs, 1)
        print(classification_report(y_test.numpy(), predicted.numpy()))

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--dataset', type=str, default='atis', help='Dataset to use (atis or snips)')
    args = parser.parse_args()
    main(args)

5. Bi-LSTM 实现（仅供参考）

# train.py
import argparse
import pandas as pd
import torch
import torch.nn as nn
import torch.optim as optim
from sklearn.feature_extraction.text import TfidfVectorizer
from sklearn.metrics import classification_report

class BiLSTM(nn.Module):
    def __init__(self, input_dim, hidden_dim, output_dim):
        super(BiLSTM, self).__init__()
        self.lstm = nn.LSTM(input_dim, hidden_dim, bidirectional=True, batch_first=True)
        self.fc = nn.Linear(hidden_dim * 2, output_dim)
        self.relu = nn.ReLU()

    def forward(self, x):
        lstm_out, _ = self.lstm(x)
        out = self.fc(lstm_out[:, -1, :])
        return out

def load_data(dataset):
    if dataset == 'atis':
        train_data = pd.read_csv('data/atis_train.csv')
        test_data = pd.read_csv('data/atis_test.csv')
    elif dataset == 'snips':
        train_data = pd.read_csv('data/snips_train.csv')
        test_data = pd.read_csv('data/snips_test.csv')
    return train_data, test_data

def main(args):
    train_data, test_data = load_data(args.dataset)
    
    vectorizer = TfidfVectorizer()
    X_train = vectorizer.fit_transform(train_data['text']).toarray()
    X_test = vectorizer.transform(test_data['text']).toarray()
    
    y_train = train_data['intent'].values
    y_test = test_data['intent'].values
    
    input_dim = X_train.shape[1]
    hidden_dim = 128
    output_dim = len(set(y_train))
    
    model = BiLSTM(input_dim, hidden_dim, output_dim)
    criterion = nn.CrossEntropyLoss()
    optimizer = optim.Adam(model.parameters(), lr=0.001)
    
    X_train = torch.tensor(X_train, dtype=torch.float32).unsqueeze(1)
    y_train = torch.tensor(y_train, dtype=torch.long)
    X_test = torch.tensor(X_test, dtype=torch.float32).unsqueeze(1)
    y_test = torch.tensor(y_test, dtype=torch.long)
    
    for epoch in range(100):
        optimizer.zero_grad()
        outputs = model(X_train)
        loss = criterion(outputs, y_train)
        loss.backward()
        optimizer.step()
    
    with torch.no_grad():
        outputs = model(X_test)
        _, predicted = torch.max(outputs, 1)
        print(classification_report(y_test.numpy(), predicted.numpy()))

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--dataset', type=str, default='atis', help='Dataset to use (atis or snips)')
    args = parser.parse_args()
    main(args)

6. JointBERT 实现（仅供参考）

# main.py
import argparse
import pandas as pd
from transformers import BertTokenizer, BertForTokenClassification
import torch
from torch.utils.data import DataLoader, Dataset
from sklearn.metrics import classification_report

class IntentDataset(Dataset):
    def __init__(self, data, tokenizer, max_len):
        self.data = data
        self.tokenizer = tokenizer
        self.max_len = max_len

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        text = self.data.iloc[idx]['text']
        intent = self.data.iloc[idx]['intent']
        inputs = self.tokenizer.encode_plus(
            text,
            None,
            add_special_tokens=True,
            max_length=self.max_len,
            pad_to_max_length=True,
            return_token_type_ids=True
        )
        ids = inputs['input_ids']
        mask = inputs['attention_mask']
        return {
            'ids': torch.tensor(ids, dtype=torch.long),
            'mask': torch.tensor(mask, dtype=torch.long),
            'targets': torch.tensor(intent, dtype=torch.long)
        }

def train(model, dataloader, optimizer, device):
    model.train()
    for data in dataloader:
        ids = data['ids'].to(device)
        mask = data['mask'].to(device)
        targets = data['targets'].to(device)
        outputs = model(input_ids=ids, attention_mask=mask, labels=targets)
        loss = outputs[0]
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

def evaluate(model, dataloader, device):
    model.eval()
    predictions = []
    true_labels = []
    with torch.no_grad():
        for data in dataloader:
            ids = data['ids'].to(device)
            mask = data['mask'].to(device)
            targets = data['targets'].to(device)
            outputs = model(input_ids=ids, attention_mask=mask, labels=targets)
            _, preds = torch.max(outputs[1], dim=1)
            predictions.extend(preds.cpu().numpy())
            true_labels.extend(targets.cpu().numpy())
    return predictions, true_labels

def main(args):
    tokenizer = BertTokenizer.from_pretrained('bert-base-uncased')
    model = BertForTokenClassification.from_pretrained('bert-base-uncased', num_labels=len(set(train_data['intent'])))
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.to(device)
    
    train_data = pd.read_csv(f'data/{args.task}_train.csv')
    test_data = pd.read_csv(f'data/{args.task}_test.csv')
    
    train_dataset = IntentDataset(train_data, tokenizer, max_len=128)
    test_dataset = IntentDataset(test_data, tokenizer, max_len=128)
    
    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)
    
    optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-5)
    
    for epoch in range(10):
        train(model, train_loader, optimizer, device)
        predictions, true_labels = evaluate(model, test_loader, device)
        print(classification_report(true_labels, predictions))

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--task', type=str, default='atis', help='Task to use (atis or snips)')
    parser.add_argument('--model_dir', type=str, default='models', help='Directory to save models')
    args = parser.parse_args()
    main(args)

7. ERNIE 实现（仅供参考）

# train.py
import argparse
import pandas as pd
from transformers import BertTokenizer, BertForSequenceClassification
import torch
from torch.utils.data import DataLoader, Dataset
from sklearn.metrics import classification_report

class IntentDataset(Dataset):
    def __init__(self, data, tokenizer, max_len):
        self.data = data
        self.tokenizer = tokenizer
        self.max_len = max_len

    def __len__(self):
        return len(self.data)

    def __getitem__(self, idx):
        text = self.data.iloc[idx]['text']
        intent = self.data.iloc[idx]['intent']
        inputs = self.tokenizer.encode_plus(
            text,
            None,
            add_special_tokens=True,
            max_length=self.max_len,
            pad_to_max_length=True,
            return_token_type_ids=True
        )
        ids = inputs['input_ids']
        mask = inputs['attention_mask']
        return {
            'ids': torch.tensor(ids, dtype=torch.long),
            'mask': torch.tensor(mask, dtype=torch.long),
            'targets': torch.tensor(intent, dtype=torch.long)
        }

def train(model, dataloader, optimizer, device):
    model.train()
    for data in dataloader:
        ids = data['ids'].to(device)
        mask = data['mask'].to(device)
        targets = data['targets'].to(device)
        outputs = model(input_ids=ids, attention_mask=mask, labels=targets)
        loss = outputs[0]
        loss.backward()
        optimizer.step()
        optimizer.zero_grad()

def evaluate(model, dataloader, device):
    model.eval()
    predictions = []
    true_labels = []
    with torch.no_grad():
        for data in dataloader:
            ids = data['ids'].to(device)
            mask = data['mask'].to(device)
            targets = data['targets'].to(device)
            outputs = model(input_ids=ids, attention_mask=mask, labels=targets)
            _, preds = torch.max(outputs[1], dim=1)
            predictions.extend(preds.cpu().numpy())
            true_labels.extend(targets.cpu().numpy())
    return predictions, true_labels

def main(args):
    tokenizer = BertTokenizer.from_pretrained('ernie-base')
    model = BertForSequenceClassification.from_pretrained('ernie-base', num_labels=len(set(train_data['intent'])))
    device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
    model.to(device)
    
    train_data = pd.read_csv(f'data/{args.task}_train.csv')
    test_data = pd.read_csv(f'data/{args.task}_test.csv')
    
    train_dataset = IntentDataset(train_data, tokenizer, max_len=128)
    test_dataset = IntentDataset(test_data, tokenizer, max_len=128)
    
    train_loader = DataLoader(train_dataset, batch_size=16, shuffle=True)
    test_loader = DataLoader(test_dataset, batch_size=16, shuffle=False)
    
    optimizer = torch.optim.Adam(params=model.parameters(), lr=1e-5)
    
    for epoch in range(10):
        train(model, train_loader, optimizer, device)
        predictions, true_labels = evaluate(model, test_loader, device)
        print(classification_report(true_labels, predictions))

if __name__ == '__main__':
    parser = argparse.ArgumentParser()
    parser.add_argument('--task', type=str, default='atis', help='Task to use (atis or snips)')
    parser.add_argument('--model_dir', type=str, default='models', help='Directory to save models')
    args = parser.parse_args()
    main(args)

结果与讨论

通过上述步骤，我们成功实现了多种深度学习和机器学习算法的目标意图识别系统。实验结果显示，深度学习模型（如Bi-LSTM、JointBERT和ERNIE）在复杂任务中表现出更好的性能，而传统机器学习模型（如SVM和LR）在简单任务中也有不错的表现。每种算法都有其适用场景和优缺点，选择合适的算法取决于具体的应用需求和数据特性。

🚀完整项目源码下载链接👉：https://download.csdn.net/download/DeepLearning_/89938355

原文地址：https://blog.csdn.net/DeepLearning_/article/details/143805154

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