Answer the questions by reading the notes after the questions (i.e. by reading the paper if available online):
Can you use RNN-LSTM for Text Classification?
Can you use RNN-LSTM for Text Summarization?
Can you use CNN for Text Classification?
Can you use CNN for Text Classification?
What does CNN stand for?
What does RNN stand for?
What does CNN-LSTM stand for?
What is more used in NLP tasks: CNN or RNN-LSTM?
What are the types of CNN?
What is CNN-Static?
What is CNN-Multichannel?
What is Glove?
What is Word2Vec?
How does text-summarization by RNN-LSTM compare with human made summaries?
How can you compare the quality of summarization between summary created by RNN-LSTM and human made summaries?
Which method provided better result for text classification (CNN or RNN-LSTM) according to this paper?
What is text encoding?
Can you encode text with your own method?
What will you prefer between Glove and Word2Vec? Why and When?
What are the different types of text encoders available?
What is special about RNN than simple NN?
What is Gated RNN?
How does Gated RNN compare with Simple RNN for text classification? From: https://sitestree.com/nlp-answer-the-questions-by-reading-the-notes-after-the-questions-i-e-by-reading-the-paper-if-available-online/
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Post Data:2019-10-04 15:24:00
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