Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks

Tian, Yuanhe; Chen, Guimin; Yan, Shuicheng; Wan, Xiang

doi:10.18653/v1/2021.acl-long.344

Cited by 70 publications

(18 citation statements)

References 40 publications

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“…When graph neural networks are used for text processing, serialized text needs to be modeled first so as to introduce text structure information and then combined with downstream tasks to process text data using deep learning-based graph propagation algorithms. Graph neural networks use words as nodes and can introduce additional information to enrich features, such as dependency relationships [14], co-occurrence information between words [15], etc.…”

Section: Graphical Neural Networkmentioning

confidence: 99%

Metaphor Recognition Method based on Graph Neural Network

Zhou¹,

Yunmei²

2023

IJACSA

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This Metaphor is a very common language phenomenon. Human language often uses metaphor to express emotion, and metaphor recognition is also an important research content in the field of NLP. Official documents are a serious style and do not usually use rhetorical sentences. This paper aims to identify rhetorical metaphorical sentences in official documents. The use of metaphors in metaphorical sentences depends on the context. Based on this linguistic feature, this paper proposes a BertGAT model, which uses Bert to extract semantic features of sentences and transform the dependency relationship between Chinese text and sentences into connected graphs. Finally, the graph attention neural network is used to learn semantic features and syntactic structure information to complete sentence metaphor recognition. The proposed model is tested on the constructed domain dataset and the sentiment public dataset respectively. Experimental results show that the method proposed in this paper can effectively improve the recognition ability of metaphorical emotional sentences.

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Section: Graphical Neural Networkmentioning

confidence: 99%

Metaphor Recognition Method based on Graph Neural Network

Zhou¹,

Yunmei²

2023

IJACSA

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“…Ref. [27] proposed a dependencydriven approach for relation extraction with attentive graph convolutional networks (A-GCN). In this approach, an attention mechanism in graph convolutional networks is applied to different contextual words in the dependency tree obtained from an off-the-shelf dependency parser, to distinguish the importance of different word dependencies.…”

Section: Dependency Constraints Integrationmentioning

confidence: 99%

Addressing Syntax-Based Semantic Complementation: Incorporating Entity and Soft Dependency Constraints into Metonymy Resolution

Wang

2022

Future Internet

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State-of-the-art methods for metonymy resolution (MR) consider the sentential context by modeling the entire sentence. However, entity representation, or syntactic structure that are informative may be beneficial for identifying metonymy. Other approaches only using deep neural network fail to capture such information. To leverage both entity and syntax constraints, this paper proposes a robust model EBAGCN for metonymy resolution. First, this work extracts syntactic dependency relations under the guidance of syntactic knowledge. Then the work constructs a neural network to incorporate both entity representation and syntactic structure into better resolution representations. In this way, the proposed model alleviates the impact of noisy information from entire sentences and breaks the limit of performance on the complicated texts. Experiments on the SemEval and ReLocaR dataset show that the proposed model significantly outperforms the state-of-the-art method BERT by more than 4%. Ablation tests demonstrate that leveraging these two types of constraints benefits fine pre-trained language models in the MR task.

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“…Schlichtkrull et al [12] applied Relational Graph Convolutional Network (R-GCN) to two standard knowledge base completion tasks: Link prediction and entity classification. Tian et al [13] proposed a dependencydriven relation extraction method based on Attentive Graph Convolutional Network (A-GCN). In the medical field, Sahu et al [14] used CNN to automatically learn features, and achieved an F1 of 71.6% on the I2B2-2010 clinical relation extraction challenge dataset.…”

Section: Related Workmentioning

confidence: 99%

Joint extraction of Chinese medical entities and relations based on RoBERTa and single-module global pointer

Yang

Cui

et al. 2022

Preprint

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Background: Most Chinese joint entity and relation extraction tasks in medicine involve numerous nested entities, overlapping relations, and other challenging extraction issues. In response to these problems, some traditional methods decompose the joint extraction task into multiple steps or multiple modules, resulting in local dependency in the meantime. Methods: To alleviate this issue, we propose a joint extraction model of Chinese medical entities and relations based on RoBERTa and single-module global pointer, namely RSGP, which formulates joint extraction as a global pointer linking problem. Considering the uniqueness of Chinese language structure, we introduce the RoBERTa-wwm pre-training language model at the encoding layer to obtain a better embedding representation. Then, we represent the input sentence as a three-dimensional matrix and score each position in the matrix to prepare for the subsequent process of decoding the triples. In the end, we design a novel single-module global pointer decoding approach to alleviate the generation of redundant information. Specifically, we analyze the decoding process of single character entities individually, improving the time and space performance of RSGP to some extent. Results: In order to verify the effectiveness of our model in extracting Chinese medical entities and relations, we carry out the experiments on the public dataset, CMeIE. Experimental results show that RSGP performs significantly better on the joint extraction of Chinese medical entities and relations, and achieves state-of-the-art results compared with baseline models. Conclusion: The proposed RSGP can effectively extract entities and relations from Chinese medical texts and help to realize the structure of Chinese medical texts, so as to provide high-quality data support for the construction of Chinese medical knowledge graphs.

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Dependency-driven Relation Extraction with Attentive Graph Convolutional Networks

Cited by 70 publications

References 40 publications

Metaphor Recognition Method based on Graph Neural Network

Metaphor Recognition Method based on Graph Neural Network

Addressing Syntax-Based Semantic Complementation: Incorporating Entity and Soft Dependency Constraints into Metonymy Resolution

Joint extraction of Chinese medical entities and relations based on RoBERTa and single-module global pointer

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