Relation extraction based on semantic dependency graph

2020 ◽  
Vol 20 (1) ◽  
pp. 279-290
Author(s):  
Ming Jiang ◽  
Jiecheng He ◽  
Jianping Wu ◽  
Chengjie Qi ◽  
Min Zhang
Keyword(s):  
Relation Extraction ◽  
Dependency Graph ◽  
Semantic Dependency

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.

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