  We describe a simple approach to semantic parsing based on a tensor product
kernel. We extract two feature vectors: one for the query and one for each
candidate logical form. We then train a classifier using the tensor product of
the two vectors. Using very simple features for both, our system achieves an
average F1 score of 40.1% on the WebQuestions dataset. This is comparable to
more complex systems but is simpler to implement and runs faster.
