Gated graph convolutional network with enhanced representation and joint attention for distant supervised heterogeneous relation extraction

2021 ◽  
Author(s):  
Xiang Ying ◽  
Zechen Meng ◽  
Mankun Zhao ◽  
Mei Yu ◽  
Shirui Pan ◽  
...  
Keyword(s):  
Joint Attention ◽  
Relation Extraction ◽  
Convolutional Network

Multi-Graph Cooperative Learning Towards Distant Supervised Relation Extraction

2021 ◽  
Vol 12 (5) ◽  
pp. 1-21
Author(s):  
Changsen Yuan ◽  
Heyan Huang ◽  
Chong Feng
Keyword(s):  
Cooperative Learning ◽  
State Of The Art ◽  
Relation Extraction ◽  
Sentence Length ◽  
Universal Relation ◽  
Dependency Parsing ◽  
Convolutional Network ◽  
Syntactic Features ◽  
Use Dependency

The Graph Convolutional Network (GCN) is a universal relation extraction method that can predict relations of entity pairs by capturing sentences’ syntactic features. However, existing GCN methods often use dependency parsing to generate graph matrices and learn syntactic features. The quality of the dependency parsing will directly affect the accuracy of the graph matrix and change the whole GCN’s performance. Because of the influence of noisy words and sentence length in the distant supervised dataset, using dependency parsing on sentences causes errors and leads to unreliable information. Therefore, it is difficult to obtain credible graph matrices and relational features for some special sentences. In this article, we present a Multi-Graph Cooperative Learning model (MGCL), which focuses on extracting the reliable syntactic features of relations by different graphs and harnessing them to improve the representations of sentences. We conduct experiments on a widely used real-world dataset, and the experimental results show that our model achieves the state-of-the-art performance of relation extraction.

scholarly journals Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation

10.2196/17638 ◽  
2020 ◽  
Vol 8 (7) ◽  
pp. e17638
Author(s):  
Jian Wang ◽  
Xiaoyu Chen ◽  
Yu Zhang ◽  
Yijia Zhang ◽  
Jiabin Wen ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Dependency Graph ◽  
Feature Representation ◽  
Convolutional Network ◽  
Semantic Relationships ◽  
Syntactic Information ◽  
Development And Validation ◽  
Context Features ◽  
Document Level ◽  
F Measure

Background Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately. Objective In this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task. Methods To improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding. Results We evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively. Conclusions The GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.

scholarly journals Software Knowledge Entity Relation Extraction with Entity-Aware and Syntactic Dependency Structure Information

2021 ◽  
Vol 2021 ◽  
pp. 1-13
Author(s):  
Mingjing Tang ◽  
Tong Li ◽  
Wei Wang ◽  
Rui Zhu ◽  
Zifei Ma ◽  
...  
Keyword(s):  
Large Scale ◽  
Complex Structure ◽  
Relation Extraction ◽  
Knowledge Graph ◽  
Knowledge Domain ◽  
Convolutional Network ◽  
Knowledge Community ◽  
Structure Information ◽  
Entity Relation Extraction ◽  
Syntactic Dependency

Software knowledge community contains a large scale of software knowledge entities with complex structure and rich semantic relations. Semantic relation extraction of software knowledge entities is a critical task for software knowledge graph construction, which has an important impact on knowledge graph based tasks such as software document generation and software expert recommendation. Due to the problems of entity sparsity, relation ambiguity, and the lack of annotated dataset in user-generated content of software knowledge community, it is difficult to apply existing methods of relation extraction in the software knowledge domain. To address these issues, we propose a novel software knowledge entity relation extraction model which incorporates entity-aware information with syntactic dependency information. Bidirectional Gated Recurrent Unit (Bi-GRU) and Graph Convolutional Networks (GCN) are used to learn the features of contextual semantic representation and syntactic dependency representation, respectively. To obtain more syntactic dependency information, a weight graph convolutional network based on Newton’s cooling law is constructed by calculating a weight adjacency matrix. Specifically, an entity-aware attention mechanism is proposed to integrate the entity information and syntactic dependency information to improve the prediction performance of the model. Experiments are conducted on a dataset which is constructed based on texts of the StackOverflow and show that the proposed model has better performance than the benchmark models.

scholarly journals Relation Extraction with Convolutional Network over Learnable Syntax-Transport Graph

2020 ◽  
Vol 34 (05) ◽  
pp. 8928-8935
Author(s):  
Kai Sun ◽  
Richong Zhang ◽  
Yongyi Mao ◽  
Samuel Mensah ◽  
Xudong Liu
Keyword(s):  
Relation Extraction ◽  
Weighted Graph ◽  
Relevant Information ◽  
Irrelevant Information ◽  
Classification Task ◽  
Convolutional Network ◽  
Structure Information ◽  
Convolutional Networks ◽  
Dependency Tree ◽  
Relation Classification

A large majority of approaches have been proposed to leverage the dependency tree in the relation classification task. Recent works have focused on pruning irrelevant information from the dependency tree. The state-of-the-art Attention Guided Graph Convolutional Networks (AGGCNs) transforms the dependency tree into a weighted-graph to distinguish the relevance of nodes and edges for relation classification. However, in their approach, the graph is fully connected, which destroys the structure information of the original dependency tree. How to effectively make use of relevant information while ignoring irrelevant information from the dependency trees remains a challenge in the relation classification task. In this work, we learn to transform the dependency tree into a weighted graph by considering the syntax dependencies of the connected nodes and persisting the structure of the original dependency tree. We refer to this graph as a syntax-transport graph. We further propose a learnable syntax-transport attention graph convolutional network (LST-AGCN) which operates on the syntax-transport graph directly to distill the final representation which is sufficient for classification. Experiments on Semeval-2010 Task 8 and Tacred show our approach outperforms previous methods.

scholarly journals KGGCN: Knowledge-Guided Graph Convolutional Networks for Distantly Supervised Relation Extraction

2021 ◽  
Vol 11 (16) ◽  
pp. 7734
Author(s):  
Ningyi Mao ◽  
Wenti Huang ◽  
Hai Zhong
Keyword(s):  
Prior Knowledge ◽  
Structural Information ◽  
Relation Extraction ◽  
Attention Mechanism ◽  
Knowledge Graph ◽  
Convolutional Network ◽  
Convolutional Networks ◽  
Lexical Resource ◽  
Sentence Level ◽  
The Impact

Distantly supervised relation extraction is the most popular technique for identifying semantic relation between two entities. Most prior models only focus on the supervision information present in training sentences. In addition to training sentences, external lexical resource and knowledge graphs often contain other relevant prior knowledge. However, relation extraction models usually ignore such readily available information. Moreover, previous works only utilize a selective attention mechanism over sentences to alleviate the impact of noise, they lack the consideration of the implicit interaction between sentences with relation facts. In this paper, (1) a knowledge-guided graph convolutional network is proposed based on the word-level attention mechanism to encode the sentences. It can capture the key words and cue phrases to generate expressive sentence-level features by attending to the relation indicators obtained from the external lexical resource. (2) A knowledge-guided sentence selector is proposed, which explores the semantic and structural information of triples from knowledge graph as sentence-level knowledge attention to distinguish the importance of each individual sentence. Experimental results on two widely used datasets, NYT-FB and GDS, show that our approach is able to efficiently use the prior knowledge from the external lexical resource and knowledge graph to enhance the performance of distantly supervised relation extraction.

scholarly journals A Graph Convolutional Network with Multiple Dependency Representations for Relation Extraction

IEEE Access ◽  
2021 ◽  
pp. 1-1
Author(s):  
Yanfeng Hu ◽  
Hong Shen ◽  
Wuling Liu ◽  
Fei Min ◽  
Xue Qiao ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Convolutional Network

Auto-learning Convolution-Based Graph Convolutional Network for Medical Relation Extraction

2021 ◽  
pp. 195-207
Author(s):  
Mengyuan Qian ◽  
Jian Wang ◽  
Hongfei Lin ◽  
Di Zhao ◽  
Yijia Zhang ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Convolutional Network

scholarly journals Document-Level Biomedical Relation Extraction Using Graph Convolutional Network and Multihead Attention: Algorithm Development and Validation (Preprint)

2019 ◽  
Author(s):  
Jian Wang ◽  
Xiaoyu Chen ◽  
Yu Zhang ◽  
Yijia Zhang ◽  
Jiabin Wen ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Dependency Graph ◽  
Feature Representation ◽  
Convolutional Network ◽  
Semantic Relationships ◽  
Syntactic Information ◽  
Development And Validation ◽  
Context Features ◽  
Document Level ◽  
F Measure

BACKGROUND Automatically extracting relations between chemicals and diseases plays an important role in biomedical text mining. Chemical-disease relation (CDR) extraction aims at extracting complex semantic relationships between entities in documents, which contain intrasentence and intersentence relations. Most previous methods did not consider dependency syntactic information across the sentences, which are very valuable for the relations extraction task, in particular, for extracting the intersentence relations accurately. OBJECTIVE In this paper, we propose a novel end-to-end neural network based on the graph convolutional network (GCN) and multihead attention, which makes use of the dependency syntactic information across the sentences to improve CDR extraction task. METHODS To improve the performance of intersentence relation extraction, we constructed a document-level dependency graph to capture the dependency syntactic information across sentences. GCN is applied to capture the feature representation of the document-level dependency graph. The multihead attention mechanism is employed to learn the relatively important context features from different semantic subspaces. To enhance the input representation, the deep context representation is used in our model instead of traditional word embedding. RESULTS We evaluate our method on CDR corpus. The experimental results show that our method achieves an F-measure of 63.5%, which is superior to other state-of-the-art methods. In the intrasentence level, our method achieves a precision, recall, and F-measure of 59.1%, 81.5%, and 68.5%, respectively. In the intersentence level, our method achieves a precision, recall, and F-measure of 47.8%, 52.2%, and 49.9%, respectively. CONCLUSIONS The GCN model can effectively exploit the across sentence dependency information to improve the performance of intersentence CDR extraction. Both the deep context representation and multihead attention are helpful in the CDR extraction task.

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