Structural block driven - enhanced convolutional neural representation for relation extraction

• URL: http://arxiv.org/abs/2103.11356v1
• Date: Sun, 21 Mar 2021 10:23:44 GMT
• Title: Structural block driven - enhanced convolutional neural representation for relation extraction
• Authors: Dongsheng Wang, Prayag Tiwari, Sahil Garg, Hongyin Zhu, Peter Bruza
• Abstract summary: We propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning. We detect the essential sequential tokens associated with entities through dependency analysis, named as a structural block. We only encode the block on a block-wise and an inter-block-wise representation, utilizing multi-scale CNNs.
• Score: 11.617819771034927
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we propose a novel lightweight relation extraction approach of structural block driven - convolutional neural learning. Specifically, we detect the essential sequential tokens associated with entities through dependency analysis, named as a structural block, and only encode the block on a block-wise and an inter-block-wise representation, utilizing multi-scale CNNs. This is to 1) eliminate the noisy from irrelevant part of a sentence; meanwhile 2) enhance the relevant block representation with both block-wise and inter-block-wise semantically enriched representation. Our method has the advantage of being independent of long sentence context since we only encode the sequential tokens within a block boundary. Experiments on two datasets i.e., SemEval2010 and KBP37, demonstrate the significant advantages of our method. In particular, we achieve the new state-of-the-art performance on the KBP37 dataset; and comparable performance with the state-of-the-art on the SemEval2010 dataset.

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