Deep Language Modeling for Question Answering using Keras

This post provides an in-depth introduction to using Keras for deep language modeling. Includes sections on word embedding, characterizing recurrent and convolutional neural networks, attentional RNNs, and similarity metrics for sentence vectors. Each section includes examples on how to implement it using Keras. This post explains the code in this Github repository. Question answering has received more focus as large search engines have basically mastered general information retrieval and are starting to cover more edge cases. Question answering happens to be one of those edge cases, because it could involve a lot of syntatic nuance that doesn't get captured by standard information retrieval models, like LDA or LSI. Hypothetically, deep learning models would be better suited to this type of task because of their ability to capture higher-order syntax. Two papers, "Applying deep learning to answer selection: a study and an open task" (Feng et.