luoyangen/Multi-Label-Text-Classification

About Muti-Label Text Classification Based on Neural Network.

Deep Learning for Multi-Label Text Classification

This project is my research group project, and it is also a study of TensorFlow, Deep Learning(CNN, RNN, LSTM, etc.).

The main objective of the project is to solve the multi-label text classification problem based on Convolutional Neural Networks. Thus, the format of the data label is like [0, 1, 0, ..., 1, 1] according to the characteristics of such problem.

Requirements

• Python 3.x
• Tensorflow 1.0.0 +
• Numpy
• Gensim

Data

Research data may attract copyright protection under China law. Thus, there is only code.

实验数据属于实验室与某公司的合作项目，涉及商业机密，在此不予提供，还望谅解。

Innovation

1. Make the data support Chinese and English.(Which use gensim seems easy)
2. Can use your own pre-trained word vectors.
3. Add a new Highway Layer.
4. Add parent label bind to limit the output of the prediction label.
5. Can choose train the model directly or restore the model from checkpoint.
6. Add model test code.

Pre-trained Word Vectors

Use gensim package to pre-train my data.

Network Structure

FastText

References:

• Bag of Tricks for Efficient Text Classification

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TextCNN

References:

• Convolutional Neural Networks for Sentence Classification
• A Sensitivity Analysis of (and Practitioners' Guide to) Convolutional Neural Networks for Sentence Classification

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TextRNN

References:

• Recurrent Neural Network for Text Classification with Multi-Task Learning

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TextRCNN

References:

• Recurrent Convolutional Neural Networks for Text Classification

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TextHAN

References:

• Hierarchical Attention Networks for Document Classification

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About Me

黄威，Randolph

SCU SE Bachelor; USTC CS Master

Email: chinawolfman@hotmail.com

My Blog: randolph.pro

LinkedIn: randolph's linkedin