A fast and effective dependency graph kernel for PPI relation extraction

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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Relation extraction based on semantic dependency graph

Journal of Computational Methods in Sciences and Engineering ◽

10.3233/jcm-193723 ◽

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

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An extended dependency graph for relation extraction in biomedical texts

10.18653/v1/w15-3803 ◽

2015 ◽

Cited By ~ 9

Author(s):

Yifan Peng ◽

Samir Gupta ◽

Cathy Wu ◽

Vijay Shanker

Keyword(s):

Relation Extraction ◽

Dependency Graph ◽

Biomedical Texts

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

10.2196/preprints.17638 ◽

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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Mitigating the Effect of Out-of-Vocabulary Entity Pairs in Matrix Factorization for KB Inference

Proceedings of the Twenty-Seventh International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2018/573 ◽

2018 ◽

Cited By ~ 1

Author(s):

Prachi Jain ◽

Shikhar Murty ◽

Mausam . ◽

Soumen Chakrabarti

Keyword(s):

Knowledge Base ◽

Hybrid Model ◽

Matrix Factorization ◽

Relation Extraction ◽

Tensor Factorization ◽

Evaluation Protocol

This paper analyzes the varied performance of Matrix Factorization (MF) on the related tasks of relation extraction and knowledge-base completion, which have been unified recently into a single framework of knowledge-base inference (KBI) [Toutanova et al., 2015]. We first propose a new evaluation protocol that makes comparisons between MF and Tensor Factorization (TF) models fair. We find that this results in a steep drop in MF performance. Our analysis attributes this to the high out-of-vocabulary (OOV) rate of entity pairs in test folds of commonly-used datasets. To alleviate this issue, we propose three extensions to MF. Our best model is a TF-augmented MF model. This hybrid model is robust and obtains strong results across various KBI datasets.

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