Comparing Convolutional Neural Networks to Traditional Models for Slot Filling
Heike Adel, Benjamin Roth, Hinrich Schütze · 2016
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-ofthe-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.