Dual CNN for Relation Extraction with Knowledge-Based Attention and Word Embeddings.

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ID: 39831
2019
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Abstract
Relation extraction is the underlying critical task of textual understanding. However, the existing methods currently have defects in instance selection and lack background knowledge for entity recognition. In this paper, we propose a knowledge-based attention model, which can make full use of supervised information from a knowledge base, to select an entity. We also design a method of dual convolutional neural networks (CNNs) considering the word embedding of each word is restricted by using a single training tool. The proposed model combines a CNN with an attention mechanism. The model inserts the word embedding and supervised information from the knowledge base into the CNN, performs convolution and pooling, and combines the knowledge base and CNN in the full connection layer. Based on these processes, the model not only obtains better entity representations but also improves the performance of relation extraction with the help of rich background knowledge. The experimental results demonstrate that the proposed model achieves competitive performance.
Reference Key
li2019dualcomputational Use this key to autocite in the manuscript while using SciMatic Manuscript Manager or Thesis Manager
Authors Li, Jun;Huang, Guimin;Chen, Jianheng;Wang, Yabing;
Journal Computational Intelligence and Neuroscience
Year 2019
DOI
10.1155/2019/6789520
URL
Keywords Keywords not found

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