Exercises of Natural Language Process.
Language: Python
Tool: numpy
Feature extraction: Bag-of-word, N-gram
Data: Classify the sentiment of sentences from the Rotten Tomatoes dataset
Goal: Implement Softmax Regression to categorize different text into different sentiment.
- All code has been uploaded
Please see my CSDN blog (in Chinese) for further illustration.
Language: Python
Tool: pytorch, numpy
Feature extraction: Random word embedding, GloVe word embedding
Data: Classify the sentiment of sentences from the Rotten Tomatoes dataset
Goal: Implement Recursive Neural Network (RNN) and Convolution Neural Network (CNN) to categorize different text into different sentiment.
- All code has been uploaded
Please see my CSDN blog (in Chinese) for further illustration.
Language: Python
Tool: pytorch, numpy
Feature extraction: Random word embedding, GloVe word embedding
Data: The Stanford Natural Language Inference (SNLI)
Reference：Enhanced LSTM for Natural Language Inference
Goal: Implement ESIM mentioned in the reference above.
- All code has been uploaded
Please see my CSDN blog (in Chinese) for further illustration.
Language: Python
Tool: pytorch, numpy
Feature extraction: Random word embedding, GloVe word embedding
Data: CONLL 2003
Data Introduction: CONLL 2003
Reference：Neural Architectures for Named Entity Recognition
End-to-end Sequence Labeling via Bi-directional LSTM-CNNs-CRF
Goal: Sequence Labeling of CONLL 2003.
- All code has been uploaded
Please see my CSDN blog (in Chinese) for further illustration.
Language: Python
Tool: pytorch, numpy
Feature extraction: Random word embedding
Data: poetryFromTang.txt
Reference：《神经网络与深度学习 chapter 6, 15》
Goal: Generating poems.
- All code has been uploaded
Please see my CSDN blog (in Chinese) for further illustration.