A comprehensive exploration of semantic relation extraction via pre-trained CNNs

Semantic relation extraction between entity pairs is a crucial task in information extraction from text. In this paper, we propose a new pre-trained network architecture for this task, and it is called the XM-CNN. The XM-CNN utilizes word embedding and position embedding information. It is designed...

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Bibliographic Details
Published in:Knowledge-based systems Vol. 194; p. 105488
Main Authors: Li, Qing, Li, Lili, Wang, Weinan, Li, Qi, Zhong, Jiang
Format: Journal Article
Language:English
Published: Amsterdam Elsevier B.V 22.04.2020
Elsevier Science Ltd
Subjects:
Attention
Computer architecture
Convolutional neural networks
Embedding
Extraction
Information retrieval
Natural language processing
Relation extraction
Semantic relation
Semantic relations
Semantics
Relation extraction
Semantic relation
Natural language processing
Convolutional neural networks
ISSN:0950-7051, 1872-7409
Online Access:Get full text
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Summary:Semantic relation extraction between entity pairs is a crucial task in information extraction from text. In this paper, we propose a new pre-trained network architecture for this task, and it is called the XM-CNN. The XM-CNN utilizes word embedding and position embedding information. It is designed to reinforce the contextual output from the MT-DNNKD pre-trained model. Our model effectively utilized an entity-aware attention mechanisms to detected the features and also adopts and applies more relation-specific pooling attention mechanisms applied to it. The experimental results show that the XM-CNN achieves state-of-the-art results on the SemEval-2010 task 8, and a thorough evaluation of the method is conducted.
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ISSN:0950-7051
1872-7409
DOI:10.1016/j.knosys.2020.105488