Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction

Hong, Yin; Liu, Yanxia; Yang, Suizhu; Zhang, Kaiwen; Wen, Aiyou; Hu, Jianjun

doi:10.1109/access.2020.2980859

Cited by 34 publications

(21 citation statements)

References 37 publications

(61 reference statements)

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“…• HRL (Takanobu et al, 2019) applies reinforcement learning to a new joint extraction paradigm, and the proposed hierarchical reinforcement learning (HRL) model decomposed the entity and relation extraction process into a two-level RL strategy hierarchy. • ImprovingGCN (Hong et al, 2020) improves on the basis of GraphRel and added the attention mechanism, allowing the model to use the weighted edge information on the graph structure. The proposed model can be used to end-to-end extract entities and relations jointly.…”

Section: Baselines and Comparison Resultsmentioning

confidence: 99%

“…ImprovingGCN (Hong et al, 2020 ) improves on the basis of GraphRel and added the attention mechanism, allowing the model to use the weighted edge information on the graph structure. The proposed model can be used to end-to-end extract entities and relations jointly.…”

Section: Experimental and Resultsmentioning

confidence: 99%

“…As shown in Table 3 , we compared our work with the above baseline model, which can verify the effectiveness of our model MA-DCGCN. Similar to GraphRel (Fu et al, 2019 ) and improving GCN (Hong et al, 2020 ), our work uses GCN. But the difference is that our model applies multi-head attention mechanism, which takes edge information in the graph structure into consideration.…”

Section: Experimental and Resultsmentioning

confidence: 99%

“…Zeng et al ( 2020 ) proposed their own improved model CopyMTL and introduced named entity task for multi-task learning based on CopyRE, thus improving the problem that CopyRE can only extract single words and cannot match multi-character entities. Hong et al ( 2020 ), based on GraphRel, proposed a new relational perceptive attention mechanism, which can acquire the representation of the relation between the span of two entities. This model utilized the characteristics of adjacent nodes and edge information when obtaining the characteristics of the encoding node.…”

Section: Related Workmentioning

confidence: 99%

“…When detecting overlapping triples, the dependency information of words and the interaction information of triples are also significant. The method ImprovingGCN (Hong et al, 2020 ) and AntNRE (Sun et al, 2019 ) consider the dependency information of words and the interaction of triplets, but they do not consider the interaction information between the probabilistic subspaces of different relation types. Thus these methods ignore the high correlation between multiple relations and entity pairs.…”

Section: Introductionmentioning

confidence: 99%

See 4 more Smart Citations

A Relational Adaptive Neural Model for Joint Entity and Relation Extraction

et al. 2021

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Relation extraction is a popular subtask in natural language processing (NLP). In the task of entity relation joint extraction, overlapping entities and multi-type relation extraction in overlapping triplets remain a challenging problem. The classification of relations by sharing the same probability space will ignore the correlation information among multiple relations. A relational-adaptive entity relation joint extraction model based on multi-head self-attention and densely connected graph convolution network (which is called MA-DCGCN) is proposed in the paper. In the model, the multi-head attention mechanism is specifically used to assign weights to multiple relation types among entities so as to ensure that the probability space of multiple relation is not mutually exclusive. This mechanism also predicts the strength of the relationship between various relationship types and entity pairs flexibly. The structure information of deeper level in the text graph is extracted by the densely connected graph convolution network, and the interaction information of entity relation is captured. To demonstrate the superior performance of our model, we conducted a variety of experiments on two widely used public datasets, NYT and WebNLG. Extensive results show that our model achieves state-of-the-art performance. Especially, the detection effect of overlapping triplets is significantly improved compared with the several existing mainstream methods.

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Section: Baselines and Comparison Resultsmentioning

confidence: 99%

Section: Experimental and Resultsmentioning

confidence: 99%

Section: Experimental and Resultsmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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A Relational Adaptive Neural Model for Joint Entity and Relation Extraction

et al. 2021

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RMAN: Relational multi-head attention neural network for joint extraction of entities and relations

et al. 2021

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A Parallel Model for Jointly Extracting Entities and Relations

Chen,

Zheng,

et al. 2024

Neural Process Lett

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Extracting relational triples from a piece of text is an essential task in knowledge graph construction. However, most existing methods either identify entities before predicting their relations, or detect relations before recognizing associated entities. This order may lead to error accumulation because once there is an error in the initial step, it will accumulate to subsequent steps. To solve this problem, we propose a parallel model for jointly extracting entities and relations, called PRE-Span, which consists of two mutually independent submodules. Specifically, candidate entities and relations are first generated by enumerating token sequences in sentences. Then, two independent submodules (Entity Extraction Module and Relation Detection Module) are designed to predict entities and relations. Finally, the predicted results of the two submodules are analyzed to select entities and relations, which are jointly decoded to obtain relational triples. The advantage of this method is that all triples can be extracted in just one step. Extensive experiments on the WebNLG*, NYT*, NYT and WebNLG datasets show that our model outperforms other baselines at 94.4%, 88.3%, 86.5% and 83.0%, respectively.

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Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction

Cited by 34 publications

References 37 publications

A Relational Adaptive Neural Model for Joint Entity and Relation Extraction

A Relational Adaptive Neural Model for Joint Entity and Relation Extraction

RMAN: Relational multi-head attention neural network for joint extraction of entities and relations

A Parallel Model for Jointly Extracting Entities and Relations

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