scholarly journals Knowing False Negatives: An Adversarial Training Method for Distantly Supervised Relation Extraction

2021 ◽  
Author(s):  
Kailong Hao ◽  
Botao Yu ◽  
Wei Hu
Keyword(s):  
Relation Extraction ◽  
Training Method ◽  
False Negatives ◽  
Adversarial Training

scholarly journals Diversity Adversarial Training against Adversarial Attack on Deep Neural Networks

Symmetry ◽  
2021 ◽  
Vol 13 (3) ◽  
pp. 428
Author(s):  
Hyun Kwon ◽  
Jun Lee
Keyword(s):  
Neural Networks ◽  
Deep Neural Networks ◽  
Diversity Training ◽  
Original Data ◽  
Training Method ◽  
Learning Framework ◽  
Adversarial Examples ◽  
Adversarial Training ◽  
Adversarial Attack ◽  
Accuracy Rates

This paper presents research focusing on visualization and pattern recognition based on computer science. Although deep neural networks demonstrate satisfactory performance regarding image and voice recognition, as well as pattern analysis and intrusion detection, they exhibit inferior performance towards adversarial examples. Noise introduction, to some degree, to the original data could lead adversarial examples to be misclassified by deep neural networks, even though they can still be deemed as normal by humans. In this paper, a robust diversity adversarial training method against adversarial attacks was demonstrated. In this approach, the target model is more robust to unknown adversarial examples, as it trains various adversarial samples. During the experiment, Tensorflow was employed as our deep learning framework, while MNIST and Fashion-MNIST were used as experimental datasets. Results revealed that the diversity training method has lowered the attack success rate by an average of 27.2 and 24.3% for various adversarial examples, while maintaining the 98.7 and 91.5% accuracy rates regarding the original data of MNIST and Fashion-MNIST.

scholarly journals A Self-Supervised Tibetan-Chinese Vocabulary Alignment Method based on Adversarial Learning

2021 ◽  
Author(s):  
Enshuai Hou ◽  
Jie zhu
Keyword(s):  
The Self ◽  
Experimental Results ◽  
Chinese Characters ◽  
Training Method ◽  
Alignment Method ◽  
Adversarial Learning ◽  
Semantic Correlation ◽  
Data Alignment ◽  
Similarity Calculation ◽  
Adversarial Training

Tibetan is a low-resource language. In order to alleviate the shortage of parallel corpus between Tibetan and Chinese, this paper uses two monolingual corpora and a small number of seed dictionaries to learn the semi-supervised method with seed dictionaries and self-supervised adversarial training method through the similarity calculation of word clusters in different embedded spaces and puts forward an improved self-supervised adversarial learning method of Tibetan and Chinese monolingual data alignment only. The experimental results are as follows. First, the experimental results of Tibetan syllables Chinese characters are not good, which reflects the weak semantic correlation between Tibetan syllables and Chinese characters; second, the seed dictionary of semi-supervised method made before 10 predicted word accuracy of 66.5 (Tibetan - Chinese) and 74.8 (Chinese - Tibetan) results, to improve the self-supervision methods in both language directions have reached 53.5 accuracy.

scholarly journals Adversarial Training for Relation Extraction

2017 ◽  
Author(s):  
Yi Wu ◽  
David Bamman ◽  
Stuart Russell
Keyword(s):  
Relation Extraction ◽  
Adversarial Training

scholarly journals An investigation of single-domain and multidomain medication and adverse drug event relation extraction from electronic health record notes using advanced deep learning models

2019 ◽  
Vol 26 (7) ◽  
pp. 646-654 ◽  
Author(s):  
Fei Li ◽  
Hong Yu
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Relation Extraction ◽  
Single Domain ◽  
Drug Event ◽  
Health Record ◽  
Learning Models ◽  
Domain Models ◽  
Adversarial Training ◽  
Mlp Model

Abstract Objective We aim to evaluate the effectiveness of advanced deep learning models (eg, capsule network [CapNet], adversarial training [ADV]) for single-domain and multidomain relation extraction from electronic health record (EHR) notes. Materials and Methods We built multiple deep learning models with increased complexity, namely a multilayer perceptron (MLP) model and a CapNet model for single-domain relation extraction and fully shared (FS), shared-private (SP), and adversarial training (ADV) modes for multidomain relation extraction. Our models were evaluated in 2 ways: first, we compared our models using our expert-annotated cancer (the MADE1.0 corpus) and cardio corpora; second, we compared our models with the systems in the MADE1.0 and i2b2 challenges. Results Multidomain models outperform single-domain models by 0.7%-1.4% in F1 (t test P < .05), but the results of FS, SP, and ADV modes are mixed. Our results show that the MLP model generally outperforms the CapNet model by 0.1%-1.0% in F1. In the comparisons with other systems, the CapNet model achieves the state-of-the-art result (87.2% in F1) in the cancer corpus and the MLP model generally outperforms MedEx in the cancer, cardiovascular diseases, and i2b2 corpora. Conclusions Our MLP or CapNet model generally outperforms other state-of-the-art systems in medication and adverse drug event relation extraction. Multidomain models perform better than single-domain models. However, neither the SP nor the ADV mode can always outperform the FS mode significantly. Moreover, the CapNet model is not superior to the MLP model for our corpora.

scholarly journals Genre Separation Network with Adversarial Training for Cross-genre Relation Extraction

2018 ◽  
Author(s):  
Ge Shi ◽  
Chong Feng ◽  
Lifu Huang ◽  
Boliang Zhang ◽  
Heng Ji ◽  
...  
Keyword(s):  
Relation Extraction ◽  
Adversarial Training
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