1 research outputs found
Comparing Convolutional Neural Networks to Traditional Models for Slot Filling
We address relation classification in the context of slot filling, the task
of finding and evaluating fillers like "Steve Jobs" for the slot X in "X
founded Apple". We propose a convolutional neural network which splits the
input sentence into three parts according to the relation arguments and compare
it to state-of-the-art and traditional approaches of relation classification.
Finally, we combine different methods and show that the combination is better
than individual approaches. We also analyze the effect of genre differences on
performance.Comment: NAACL 201