Applying the four step "Embed, Encode, Attend, Predict" framework to predict document similarity
summary
A four‑step recipe—Embed, Encode, Attend, Predict—is implemented with the Keras Deep Learning Library and custom attention layer code; word embeddings (GloVe/fastText) supply pretrained vectors, bidirectional GRU/LSTM do encoding, and a shallow classifier predicts labels. On 20 Newsgroups a bag‑of‑words baseline hits ~71% accuracy, a hierarchical encode model ~77%, and adding attention reaches ~82.4%. Document‑similarity experiments use TF‑IDF pairs but show tiny cosine gaps (~0.02) and poor gains from attention, suggesting noisy data or an implementation issue.
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chapters (14)
- 0:00Embed Encode Attend Predict framework
- 1:24NLP pipelines before deep learning
- 5:21Word embeddings in NLP
- 8:07Embed step details
- 9:10Encode step with bidirectional LSTMs
- 10:13Attention mechanisms
- 16:53Attention types and implementations
- 26:06Predict step and model finalization
- 27:09Document classification example
- 33:27Attention for document classification
- 36:01Document similarity with hierarchical models
- 38:48Sentence similarity model results
- 41:40Summary of framework
- 43:05References
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