Graph Adaptation Network with Domain-Specific Word Alignment for Cross-Domain Relation Extraction release_bjjnzbqdabfspmpfl7nkabtn7a

by Wang Zhe, 博 闫, chunhua wu, Bin Wu, Xiujuan Wang, Kangfeng Zheng

Published in Sensors by MDPI AG.

2020   Volume 20, Issue 24, p7180

Abstract

Cross-domain relation extraction has become an essential approach when target domain lacking labeled data. Most existing works adapted relation extraction models from the source domain to target domain through aligning sequential features, but failed to transfer non-local and non-sequential features such as word co-occurrence which are also critical for cross-domain relation extraction. To address this issue, in this paper, we propose a novel tripartite graph architecture to adapt non-local features when there is no labeled data in the target domain. The graph uses domain words as nodes to model the co-occurrence relation between domain-specific words and domain-independent words. Through graph convolutions on the tripartite graph, the information of domain-specific words is propagated so that the word representation can be fine-tuned to align domain-specific features. In addition, unlike the traditional graph structure, the weights of edges innovatively combine fixed weight and dynamic weight, to capture the global non-local features and avoid introducing noise to word representation. Experiments on three domains of ACE2005 datasets show that our method outperforms the state-of-the-art models by a big margin.
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Type  article-journal
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Date   2020-12-15
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DOI  10.3390/s20247180
PubMed  33333844
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