Multi-level Attention-Based Neural Networks for Distant Supervised Relation Extraction
|Title:||Multi-level Attention-Based Neural Networks for Distant Supervised Relation Extraction||Authors:||Yang, Linyi; Ng, Tin Lok James; Mooney, Catherine; Dong, Ruihai||Permanent link:||http://hdl.handle.net/10197/9303||Date:||8-Dec-2017||Online since:||2018-04-09T09:26:20Z||Abstract:||We propose a multi-level attention-based neural network forrelation extraction based on the work of Lin et al. to alleviate the problemof wrong labelling in distant supervision. In this paper, we first adoptgated recurrent units to represent the semantic information. Then, weintroduce a customized multi-level attention mechanism, which is expectedto reduce the weights of noisy words and sentences. Experimentalresults on a real-world dataset show that our model achieves significantimprovement on relation extraction tasks compared to both traditionalfeature-based models and existing neural network-based methods||Funding Details:||Science Foundation Ireland||Type of material:||Conference Publication||Publisher:||Insight Centre||Keywords:||Relation extraction; Distant supervision; Word-level attention||Other versions:||http://aics2017.dit.ie/papers.html||Language:||en||Status of Item:||Peer reviewed||Conference Details:||25th Irish Conference on Artificial Intelligence and Cognitive Science, Dublin, Ireland, 7-8 December 2017||This item is made available under a Creative Commons License:||https://creativecommons.org/licenses/by-nc-nd/3.0/ie/|
|Appears in Collections:||Insight Research Collection|
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