Name
Abstract | ||
|---|---|---|
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. |
Year | DOI | Venue |
|---|---|---|
2018 | 10.1016/j.neunet.2018.08.016 | Neural Networks |
Keywords | Field | DocType |
Relation extraction,Distant supervision,Hierarchical attention,Piecewise convolutional neural networks | Selective attention,Artificial intelligence,Sentence,Mathematics,Machine learning,Relationship extraction | Journal |
Volume | Issue | ISSN |
108 | 1 | 0893-6080 |
Citations | PageRank | References |
2 | 0.37 | 22 |
Authors | ||
6 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| P. Zhou | 1 | 372 | 26.80 |
| Jiaming Xu | 2 | 284 | 35.34 |
| Zhenyu Qi | 3 | 337 | 22.70 |
| Hongyun Bao | 4 | 69 | 4.32 |
| Zhineng Chen | 5 | 192 | 25.29 |
| Bo Xu | 6 | 112 | 20.58 |