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Multi-Stream Semantics-Guided Dynamic Aggregation Graph Convolution Networks to Extract Overlapping Relations
2021
IEEE Access
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graph convolution network (SG-DAGCN) to realize the extraction of overlapping relations. ...
The proposed model constructs the entity relation graphs by enumerating the possible candidates and external auxiliary information and adaptively manages the relevant substructure. ...
ACKNOWLEDGMENT The authors would like to thank the anonymous reviewers for their comments and helpful suggestions. ...
doi:10.1109/access.2021.3062231
fatcat:zu4rrzkalfcsxdol7x63lzhadu
A Relational Adaptive Neural Model for Joint Entity and Relation Extraction
2021
Frontiers in Neurorobotics
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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. ...
In the task of entity relation joint extraction, overlapping entities and multi-type relation extraction in overlapping triplets remain a challenging problem. ...
CONCLUSION We propose a new joint entity and relation extraction model based on densely connected graph convolutional network (DCGCN). ...
doi:10.3389/fnbot.2021.635492
pmid:33796016
pmcid:PMC8008121
fatcat:gicxhwevdfgodcephdlnw5xbrm
Improving Graph Convolutional Networks Based on Relation-aware Attention for End-to-End Relation Extraction
2020
IEEE Access
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INDEX TERMS Graph convolutional network, joint extraction of entities and relations, attention, sequential labelling. 1 In this paper, overlapping relation = overlapping triplets. ...
In this paper, we present a novel end-to-end neural model based on graph convolutional networks (GCN) for jointly extracting entities and relations between them. ...
A BREIF OF GCN As an adaptation of convolutional neural network [35] , the graph convolutional network [13] encodes each node representation based on adjacency nodes. ...
doi:10.1109/access.2020.2980859
fatcat:ueeiv74fmndkpgthcofndyktta
RECA: Relation Extraction Based on Cross-Attention Neural Network
2022
Electronics
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Nevertheless, the mainstream relation-extraction methods, the pipeline method and the joint method, ignore the dependency between the subject entity and the object entity. ...
Extracting entities and relations, as a crucial part of many tasks in natural language processing, transforms the unstructured text information into structured information and provides corresponding data ...
[10] addressed the overlapping problem by treating words in sentences as nodes of a graph and relations between the entities as edges of a graph and then introducing a graph convolutional neural network ...
doi:10.3390/electronics11142161
fatcat:wcarjdx5pvaipk5bic654ee3du
GraphRel: Modeling Text as Relational Graphs for Joint Entity and Relation Extraction
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
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In this paper, we present GraphRel, an end-to-end relation extraction model which uses graph convolutional networks (GCNs) to jointly learn named entities and relations. ...
Also, GraphRel outperforms previous work by 3.2% and 5.8% (F1 score), achieving a new state-of-the-art for relation extraction. ...
Review of GCN As convolutional neural network (CNN), Graph Convolutional Network (GCN) (Kipf and Welling, 2017) convolves the features of neighboring nodes and also propagates the information of a node ...
doi:10.18653/v1/p19-1136
dblp:conf/acl/FuLM19
fatcat:rrmpsowi7fe6xp32i2tuw5bgte
Extraction of Joint Entity and Relationships with Soft Pruning and GlobalPointer
2022
Applied Sciences
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In recent years, scholars have paid increasing attention to the joint entity and relation extraction. However, the most difficult aspect of joint extraction is extracting overlapping triples. ...
Specifically, to address the lack of information caused by the rule-based pruning strategies, we utilize the Gaussian Graph Generator and the attention-guiding layer to construct a fully connected graph ...
Conflicts of Interest: There are no conflict to declare. Appl. Sci. 2022, 12, 6361 ...
doi:10.3390/app12136361
fatcat:773ukym7c5g55bbvkrsrkei7c4
Joint Entity-Relation Extraction via Improved Graph Attention Networks
2020
Symmetry
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To mitigate the over-smoothing problem of graph convolutional networks, inspired by matrix factorization, we improved the graph attention network by designing a new multi-head attention mechanism and sharing ...
To solve this problem, we proposed a novel joint model named extracting Entity-Relations viaImproved Graph Attention networks (ERIGAT), which enhances the ability of the relation extraction task. ...
Acknowledgments: The authors are very thankful to the editor and referees for their valuable comments and suggestions for improving the paper. ...
doi:10.3390/sym12101746
fatcat:owevexuchjcgpf5eghrbkqr2wm
Deep Neural Networks for Relation Extraction
[article]
2021
arXiv
pre-print
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Finally, we propose a hierarchical entity graph convolutional network for relation extraction across documents. ...
Next, we propose two joint entity and relation extraction frameworks based on encoder-decoder architecture. ...
F1 score drops by 1.3% after removing the entity mentionlevel graph convolutional network (-EMGCN). F1 score drops by 0.3% after removing the unified entity-level graph convolutional network (-EGCN). ...
arXiv:2104.01799v1
fatcat:vmatz7gxazd4xnm2oprncd5mm4
Deep Neural Approaches to Relation Triplets Extraction: A Comprehensive Survey
[article]
2021
arXiv
pre-print
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Regarding neural architectures, we cover convolutional models, recurrent network models, attention network models, and graph convolutional models in this survey. ...
Recently, with the advances made in continuous representation of words (word embeddings) and deep neural architectures, many research works are published in the area of relation extraction and it is very ...
Zeng et al. (2015) introduced a piecewise convolutional neural network (PCNN) to improve relation extraction. ...
arXiv:2103.16929v1
fatcat:a25435weifccdknduaiilk7ufy
A Span-Based Model for Joint Overlapped and Discontinuous Named Entity Recognition
[article]
2021
arXiv
pre-print
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In this way, we can recognize not only discontinuous entities, and meanwhile doubly check the overlapped entities. As a whole, our model can be regarded as a relation extraction paradigm essentially. ...
Second, we perform relation classification to judge whether a given pair of entity fragments to be overlapping or succession. ...
Acknowledgments We thank the reviewers for their comments and recommendation. This work is supported by the National Natural Science ...
arXiv:2106.14373v1
fatcat:lyyvifwsjzgbhcv4nct75xqm6m
TDRE: A Tensor Decomposition Based Approach for Relation Extraction
[article]
2020
arXiv
pre-print
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Extracting entity pairs along with relation types from unstructured texts is a fundamental subtask of information extraction. ...
According to effective decomposition methods, we propose the Tensor Decomposition based Relation Extraction (TDRE) approach which is able to extract overlapping triplets and avoid detecting unnecessary ...
[2] made fully uses of the relevance between entity and relation types by building entity-relation graph to catch the combination of entity pairs and valid relation. Guo et al. ...
arXiv:2010.07533v1
fatcat:jxesupcclrehham46e57bef74u
Complex Causal Extraction of Fusion of Entity Location Sensing and Graph Attention Networks
2022
Information
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Based on this, a causal model, RPA-GCN (relation position and attention-graph convolutional networks), is constructed, incorporating GAT (graph attention network) and entity location perception. ...
At present, there is no uniform definition of annotation schemes for causal extraction, and existing methods are limited by the dependence of relations on long spans, which makes complex sentences such ...
Dixit [22] constructs a span-level graph for joint detection of overlapping entities and relations. ...
doi:10.3390/info13080364
fatcat:ehadgpncf5htnlz5r5hutj5lui
A Method about Building Deep Knowledge Graph for the Plant Insect Pest and Disease (DKG-PIPD)
2021
IEEE Access
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efficiency, but also solved the problem of one-versus-many overlapping relation extraction. ...
Moreover, the related work in this paper first introduced the general architecture required for the building of knowledge graph, and then summarized its key points, that is, named entity recognition, entity ...
pairs, which can improve the problem of entity overlap and relation overlap to some extent. ...
doi:10.1109/access.2021.3116467
fatcat:vyoluv7ln5ebnlrm3ogut32eoa
A Deep Neural Network Model for Joint Entity and Relation Extraction
2019
IEEE Access
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Joint extraction of entities and their relations from the text is an essential issue in automatic knowledge graph construction, which is also known as the joint extraction of relational triplets. ...
INDEX TERMS Automatic knowledge graph construction, entity and relation extraction, deep neural networks, natural language processing, pointer networks, relational triplet extraction, sequence-to-sequence ...
Zeng and Dr. K. Liu from the Institute of Automation of the Chinese Academy of Sciences for their helpful discussion on this article. VOLUME 7, 2019 ...
doi:10.1109/access.2019.2949086
fatcat:u2kruchoufalreo3u7eegu2dd4
RTJTN: Relational Triplet Joint Tagging Network for Joint Entity and Relation Extraction
2021
Computational Intelligence and Neuroscience
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In the joint tagging layer, instead of extracting entity and relation separately, we propose a tagging method that allows the model to simultaneously extract entities and relations in unstructured sentences ...
In this paper, we introduce a relational triplet joint tagging network (RTJTN), which is divided into joint entities and relations tagging layer and relational triplet judgment layer. ...
Conflicts of Interest e authors declare that there are no conflicts of interest regarding the publication of this paper. ...
doi:10.1155/2021/3447473
pmid:34697539
pmcid:PMC8541843
fatcat:h5bcw3k4pbhdrhi7m4adt6dzsy
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