scholarly journals CID-GCN: An Effective Graph Convolutional Networks for Chemical-Induced Disease Relation Extraction

2021 ◽  
Vol 12 ◽  
Author(s):  
Daojian Zeng ◽  
Chao Zhao ◽  
Zhe Quan
Keyword(s):  
Drug Development ◽  
Relation Extraction ◽  
Automatic Extraction ◽  
Disease Treatment ◽  
Severe Limitation ◽  
Convolutional Networks ◽  
Convolution Operation ◽  
Single Sentence ◽  
Unstructured Text ◽  
Gating Mechanism

Automatic extraction of chemical-induced disease (CID) relation from unstructured text is of essential importance for disease treatment and drug development. In this task, some relational facts can only be inferred from the document rather than single sentence. Recently, researchers investigate graph-based approaches to extract relations across sentences. It iteratively combines the information from neighbor nodes to model the interactions in entity mentions that exist in different sentences. Despite their success, one severe limitation of the graph-based approaches is the over-smoothing problem, which decreases the model distinguishing ability. In this paper, we propose CID-GCN, an effective Graph Convolutional Networks (GCNs) with gating mechanism, for CID relation extraction. Specifically, we construct a heterogeneous graph which contains mention, sentence and entity nodes. Then, the graph convolution operation is employed to aggregate interactive information on the constructed graph. Particularly, we combine gating mechanism with the graph convolution operation to address the over-smoothing problem. The experimental results demonstrate that our approach significantly outperforms the baselines.

scholarly journals Building Multi-Feature Fusion Refined Network for Building Extraction from High-Resolution Remote Sensing Images

2021 ◽  
Vol 13 (14) ◽  
pp. 2794
Author(s):  
Shuhao Ran ◽  
Xianjun Gao ◽  
Yuanwei Yang ◽  
Shaohua Li ◽  
Guangbin Zhang ◽  
...  
Keyword(s):  
Feature Fusion ◽  
Small Scale ◽  
Automatic Extraction ◽  
Learning Approaches ◽  
Building Extraction ◽  
Visual Interpretation ◽  
Learning Capacity ◽  
Convolutional Networks ◽  
Fully Convolutional Networks ◽  
Great Progress

Deep learning approaches have been widely used in building automatic extraction tasks and have made great progress in recent years. However, the missing detection and wrong detection causing by spectrum confusion is still a great challenge. The existing fully convolutional networks (FCNs) cannot effectively distinguish whether the feature differences are from one building or the building and its adjacent non-building objects. In order to overcome the limitations, a building multi-feature fusion refined network (BMFR-Net) was presented in this paper to extract buildings accurately and completely. BMFR-Net is based on an encoding and decoding structure, mainly consisting of two parts: the continuous atrous convolution pyramid (CACP) module and the multiscale output fusion constraint (MOFC) structure. The CACP module is positioned at the end of the contracting path and it effectively minimizes the loss of effective information in multiscale feature extraction and fusion by using parallel continuous small-scale atrous convolution. To improve the ability to aggregate semantic information from the context, the MOFC structure performs predictive output at each stage of the expanding path and integrates the results into the network. Furthermore, the multilevel joint weighted loss function effectively updates parameters well away from the output layer, enhancing the learning capacity of the network for low-level abstract features. The experimental results demonstrate that the proposed BMFR-Net outperforms the other five state-of-the-art approaches in both visual interpretation and quantitative evaluation.

scholarly journals Self-Attention Enhanced Selective Gate with Entity-Aware Embedding for Distantly Supervised Relation Extraction

2020 ◽  
Vol 34 (05) ◽  
pp. 8269-8276
Author(s):  
Yang Li ◽  
Guodong Long ◽  
Tao Shen ◽  
Tianyi Zhou ◽  
Lina Yao ◽  
...  
Keyword(s):  
Selective Attention ◽  
Relation Extraction ◽  
Attention Mechanism ◽  
Position Information ◽  
Distant Supervision ◽  
Gating Mechanism ◽  
The Rich ◽  
Training Examples ◽  
Relation Classification ◽  
Relative Position Information

Distantly supervised relation extraction intrinsically suffers from noisy labels due to the strong assumption of distant supervision. Most prior works adopt a selective attention mechanism over sentences in a bag to denoise from wrongly labeled data, which however could be incompetent when there is only one sentence in a bag. In this paper, we propose a brand-new light-weight neural framework to address the distantly supervised relation extraction problem and alleviate the defects in previous selective attention framework. Specifically, in the proposed framework, 1) we use an entity-aware word embedding method to integrate both relative position information and head/tail entity embeddings, aiming to highlight the essence of entities for this task; 2) we develop a self-attention mechanism to capture the rich contextual dependencies as a complement for local dependencies captured by piecewise CNN; and 3) instead of using selective attention, we design a pooling-equipped gate, which is based on rich contextual representations, as an aggregator to generate bag-level representation for final relation classification. Compared to selective attention, one major advantage of the proposed gating mechanism is that, it performs stably and promisingly even if only one sentence appears in a bag and thus keeps the consistency across all training examples. The experiments on NYT dataset demonstrate that our approach achieves a new state-of-the-art performance in terms of both AUC and top-n precision metrics.

scholarly journals PPPred: Classifying Protein-phenotype Co-mentions Extracted from Biomedical Literature

2019 ◽  
Author(s):  
Morteza Pourreza Shahri ◽  
Mandi M. Roe ◽  
Gillian Reynolds ◽  
Indika Kahanda
Keyword(s):  
Relation Extraction ◽  
Biomedical Literature ◽  
Supervised Machine Learning ◽  
Human Phenotype ◽  
Unstructured Text ◽  
Gold Standard Dataset ◽  
Sentence Level ◽  
Machine Learning Approach ◽  
Human Proteins ◽  
Biomedical Relation Extraction

ABSTRACTThe MEDLINE database provides an extensive source of scientific articles and heterogeneous biomedical information in the form of unstructured text. One of the most important knowledge present within articles are the relations between human proteins and their phenotypes, which can stay hidden due to the exponential growth of publications. This has presented a range of opportunities for the development of computational methods to extract these biomedical relations from the articles. However, currently, no such method exists for the automated extraction of relations involving human proteins and human phenotype ontology (HPO) terms. In our previous work, we developed a comprehensive database composed of all co-mentions of proteins and phenotypes. In this study, we present a supervised machine learning approach called PPPred (Protein-Phenotype Predictor) for classifying the validity of a given sentence-level co-mention. Using an in-house developed gold standard dataset, we demonstrate that PPPred significantly outperforms several baseline methods. This two-step approach of co-mention extraction and classification constitutes a complete biomedical relation extraction pipeline for extracting protein-phenotype relations.CCS CONCEPTS•Computing methodologies → Information extraction; Supervised learning by classification; •Applied computing →Bioinformatics;

scholarly journals Global Context-enhanced Graph Convolutional Networks for Document-level Relation Extraction

2020 ◽  
Author(s):  
Huiwei Zhou ◽  
Yibin Xu ◽  
Weihong Yao ◽  
Zhe Liu ◽  
Chengkun Lang ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Global Context ◽  
Convolutional Networks ◽  
Document Level

scholarly journals Improving Graph Convolutional Networks Based on Relation-Aware Attention for End-to-End Relation Extraction

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 51315-51323 ◽  
Author(s):  
Yin Hong ◽  
Yanxia Liu ◽  
Suizhu Yang ◽  
Kaiwen Zhang ◽  
Aiqing Wen ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Convolutional Networks ◽  
End To End
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