Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN
Relation Classification Using Segment-Level Attention-based CNN and Dependency-based RNN
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DOI：
10.18653/v1/n19-1286
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发表时间：
2019-06
期刊：
影响因子：
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通讯作者：
Van-Hien Tran;Van-Thuy Phi;Hiroyuki Shindo;Yuji Matsumoto
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文献类型：
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作者：
Van-Hien Tran;Van-Thuy Phi;Hiroyuki Shindo;Yuji Matsumoto
Recently, relation classification has gained much success by exploiting deep neural networks. In this paper, we propose a new model effectively combining Segment-level Attention-based Convolutional Neural Networks (SACNNs) and Dependency-based Recurrent Neural Networks (DepRNNs). While SACNNs allow the model to selectively focus on the important information segment from the raw sequence, DepRNNs help to handle the long-distance relations from the shortest dependency path of relation entities. Experiments on the SemEval-2010 Task 8 dataset show that our model is comparable to the state-of-the-art without using any external lexical features.
