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Distant supervision for relation extraction with hierarchical selective attention.
- Source :
-
Neural Networks . Dec2018, Vol. 108, p240-247. 8p. - Publication Year :
- 2018
-
Abstract
- 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. [ABSTRACT FROM AUTHOR]
Details
- Language :
- English
- ISSN :
- 08936080
- Volume :
- 108
- Database :
- Academic Search Index
- Journal :
- Neural Networks
- Publication Type :
- Academic Journal
- Accession number :
- 133047556
- Full Text :
- https://doi.org/10.1016/j.neunet.2018.08.016