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Does it make sense to add word embeddings as additional features for LSTM model?

I have an LSTM model. This model takes as input tokens. Those tokens represent XML markups extracted from some XML files. My model is working fine. However, I want to optimize it by adding word ...
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How can I combine word2vec with tf-idf to have concatenated features?

I want to develop a focused crawler using deep reinforcement learning and a priority queue that will work as the crawler frontier. I reckon using x = (state, action)...