arXiv:1802.08949
OhioState at SemEval-2018 Task 7: Exploiting Data Augmentation for Relation Classification in Scientific Papers using Piecewise Convolutional Neural Networks
원문 초록
We describe our system for SemEval-2018 Shared Task on Semantic Relation Extraction and Classification in Scientific Papers where we focus on the Classification task. Our simple piecewise convolution neural encoder performs decently in an end to end manner. A simple inter-task data augmentation signifi- cantly boosts the performance of the model. Our best-performing systems stood 8th out of 20 teams on the classification task on noisy data and 12th out of 28 teams on the classification task on clean data.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
AI-Enhanced Pediatric Pneumonia Detection: A CNN-Based Approach Using Data Augmentation and Generative Adversarial Networks (GANs)
Improving Model Performance and Removing the Class Imbalance Problem Using Augmentation
Augmentation Techniques Analysis with Removal of Class Imbalance Using PyTorch for Intel Scene Dataset
Discrete Wavelet Transform for Generative Adversarial Network to Identify Drivers Using Gyroscope and Accelerometer Sensors
DocXClassifier: High Performance Explainable Deep Network for Document Image Classification
Generative Adversarial Minority Oversampling for Spectral-Spatial Hyperspectral Image Classification
토론
이 논문과 연결된 포럼 글이 아직 없습니다.