NLP Multi-label Classification Project

This project aims to classify text data into multiple categories using various deep learning architectures like Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Bidirectional RNNs (Bi-RNN), Long Short-Term Memory (LSTM), Bidirectional LSTM (Bi-LSTM), Gated Recurrent Unit (GRU), and Bidirectional GRU (Bi-GRU). The dataset used for this project consists of text documents with multiple labels.

Project Overview

Data Preprocessing

• The dataset was loaded from a CSV file containing text data and corresponding labels.
• Preprocessing steps included removing duplicate entries, filtering out rows with all-zero labels, and removing punctuation and pure numerical values from the text.
• The dataset was split into training and testing sets.

Note: to get this data search for SemEval 2018 data on google

Tokenization and Padding

• Tokenization was performed using the Keras Tokenizer to convert text data into sequences of integers.
• Sequences were padded to ensure uniform length using the Keras pad_sequences function.

Model Architectures

1. Convolutional Neural Network (CNN)

   • Utilized a CNN architecture with an embedding layer, convolutional layer, batch normalization, dropout, and dense layers.
   • Implemented with the Keras Sequential API.

2. Recurrent Neural Network (RNN)

   • Employed a simple RNN architecture with similar layers as CNN.
   • Configured with Keras Sequential API.

3. Bidirectional RNN (Bi-RNN)

   • Integrated bidirectional RNN layers to capture information from both forward and backward directions.

4. Long Short-Term Memory (LSTM)

   • Implemented LSTM architecture to handle long-term dependencies in text data.

5. Bidirectional LSTM (Bi-LSTM)

   • Enhanced LSTM with bidirectional processing for improved context understanding.

6. Gated Recurrent Unit (GRU)

   • Utilized GRU architecture as an alternative to LSTM for capturing long-term dependencies.

7. Bidirectional GRU (Bi-GRU)

   • Incorporated bidirectional processing into GRU architecture.

Training and Evaluation

• Models were compiled with binary cross-entropy loss and Adam optimizer.
• Training was conducted with a specified number of epochs and early stopping to prevent overfitting.
• Models were evaluated on both training and testing datasets for accuracy and loss metrics.

Results

• Each model's training and testing performance metrics were recorded and compared.
• Plots were generated to visualize the training and validation accuracy and loss over epochs.

Conclusion

• The project demonstrates the effectiveness of various deep learning architectures for multi-label text classification tasks.
• Future work may involve experimenting with hyperparameters, exploring different architectures, or incorporating ensemble methods for further improvement.

Dependencies

• TensorFlow
• Keras
• Pandas
• NumPy
• Matplotlib
• Seaborn
• scikit-learn