Distant supervision for relation extraction with hierarchical selective attention
Distant supervised relation extraction is an important task in the field of natural language processing. There are two main shortcomings for most state of the art methods. One is that they take all sentences of an entity pair as input, which would result in a large computational cost. But in fact, few of most relevant sentences are enough to recognize the relation of an entity pair. To tackle these problems, we propose a novel hierarchical selective attention network for relation extraction under distant supervision. Our model first selects most relevant sentences by taking coarse sentence level attention on all sentences of an entity pair and then employs word level attention to construct sentence representations and fine sentence level attention to aggregate these sentence representations. Experimental results on a widely used dataset demonstrate that our method performs significantly better than most of existing methods.
Neural Networks