  Machine comprehension plays an essential role in NLP and has been widely
explored with dataset like MCTest. However, this dataset is too simple and too
small for learning true reasoning abilities. \cite{hermann2015teaching}
therefore release a large scale news article dataset and propose a deep LSTM
reader system for machine comprehension. However, the training process is
expensive. We therefore try feature-engineered approach with semantics on the
new dataset to see how traditional machine learning technique and semantics can
help with machine comprehension. Meanwhile, our proposed L2R reader system
achieves good performance with efficiency and less training data.
