Multi-Relation Extraction via A Global-Local Graph Convolutional Network

Harry H. Cheng

doi:10.1109/TBDATA.2022.3144151

Multi-Relation Extraction via A Global-Local Graph Convolutional Network

2022-01-01

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DOI：10.1109/TBDATA.2022.3144151

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Relation extraction (RE) extracts the semantic relations among entities in a sentence, which converts the unstructured text into structured and easy-to-understand information. Although RE has been studied over decades, it still faces two kinds of research challenges that are not well addressed thus far: 1) joint consideration of the global sentence structure and the local entity interaction, and 2) effective solution to the overlapping triplets within the same sentence. To tackle these issues, in this paper, we present global-local graph-based convolutional network towards multi-relation extraction, GAME for short. In particular, we devise two layers of graph convolutional network (GCN) with different structures to complete the feature extraction, which effectively improves the capability of relation extraction. Moreover, we implement the GCN layers via the pure GCN model and graph attention network respectively for further comparison. Besides, we adopt a classification strategy to extract relation among entity pairs, assisting in solving the more complicated problem of overlapping triplets in RE. Extensive experiments have been conducted on two widely-used benchmark datasets, demonstrating that our model significantly outperforms several state-of-the-art methods. As a side product, we have released our data, codes and parameter settings to facilitate other researchers.

Keywords:

Feature extraction

Data mining

Convolutional codes

Games

Task analysis

Big Data

Semantics

Relation extraction

overlapping triplets

graph convolution

natural language processing

Multi-Relation Extraction via A Global-Local Graph Convolutional Network

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