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Exploit Feature and Relation Hierarchy for Relation Extraction
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AI
DOI：10.1109/TASLP.2022.3153256.png)
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Abstract
En 中文
Existing methods in relation extraction have leveraged the lexical features in the word sequence and the syntactic features in the parse tree. Though effective, the lexical features extracted from the successive word sequence may introduce some noise that has little or no meaningful content. Meanwhile, the syntactic features are usually encoded via graph convolutional networks which have restricted receptive field. In addition, the relation between lexical and syntactic features in the representation space has been largely neglected. To address the above limitations, we propose a multi-scale representation and metric learning framework to exploit the feature and relation hierarchy for RE tasks. Methodologically, we build a lexical and syntactic feature and relation hierarchy in text data. Technically, we first develop a multi-scale convolutional neural network to aggregate the non-successive lexical patterns in the word sequence. We also design a multi-scale graph convolutional network to increase the receptive field via the coarsened syntactic graph. Moreover, we present a multi-scale metric learning paradigm to exploit both the feature-level relation between lexical and syntactic features and the sample-level relation between instances with the same or different classes. Extensive experiments on three public datasets for two RE tasks prove that our model achieves a new state-of-the-art performance.
Keywords:
Syntactics
Feature extraction
Task analysis
Measurement
Convolution
Data mining
Representation learning
Multi-scale feature learning
multi-scale metric learning
relation extraction
Journal
I
IF:
5.1
Papers:
2.6K
Citations:
1.1W
