scholarly journals Learning From How Human Correct

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

scholarly journals Learning From How Human Correct

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

scholarly journals Learning From How Human Correct

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

scholarly journals Learning From Human Correction For Data-Centric Deep Learning

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

scholarly journals Learning From Human Correction For Data-Centric Deep Learning

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

scholarly journals Learning From How Human Correct

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

scholarly journals Learning From How Human Correct For Data-Centric Deep Learning

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Learning Model ◽  
Simple Method ◽  
Know How ◽  
Novel Method ◽  
Deep Learning Model

In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and relabel them manually, meanwhile we collect the correction information. Then we present novel method to incorporate the human correction information into deep learning model. Human know how to correct noisy data. So the correction information can be inject into deep learning model. We do the experiment on our own text classification dataset, which is manually labeled, because we relabel the noisy data in our dataset for our industry application. The experiment result shows that our method improve the classification accuracy from 91.7% to 92.5%. The 91.7% baseline is based on BERT training on the corrected dataset, which is hard to surpass.

scholarly journals Medical Text Classification Using Hybrid Deep Learning Models with Multihead Attention

2021 ◽  
Vol 2021 ◽  
pp. 1-16
Author(s):  
Sunil Kumar Prabhakar ◽  
Dong-Ok Won
Keyword(s):  
Deep Learning ◽  
Language Processing ◽  
Text Classification ◽  
Patient Information ◽  
Classification Accuracy ◽  
Learning Model ◽  
Training Data ◽  
Machine Learning Techniques ◽  
Medical Text ◽  
Deep Learning Model

To unlock information present in clinical description, automatic medical text classification is highly useful in the arena of natural language processing (NLP). For medical text classification tasks, machine learning techniques seem to be quite effective; however, it requires extensive effort from human side, so that the labeled training data can be created. For clinical and translational research, a huge quantity of detailed patient information, such as disease status, lab tests, medication history, side effects, and treatment outcomes, has been collected in an electronic format, and it serves as a valuable data source for further analysis. Therefore, a huge quantity of detailed patient information is present in the medical text, and it is quite a huge challenge to process it efficiently. In this work, a medical text classification paradigm, using two novel deep learning architectures, is proposed to mitigate the human efforts. The first approach is that a quad channel hybrid long short-term memory (QC-LSTM) deep learning model is implemented utilizing four channels, and the second approach is that a hybrid bidirectional gated recurrent unit (BiGRU) deep learning model with multihead attention is developed and implemented successfully. The proposed methodology is validated on two medical text datasets, and a comprehensive analysis is conducted. The best results in terms of classification accuracy of 96.72% is obtained with the proposed QC-LSTM deep learning model, and a classification accuracy of 95.76% is obtained with the proposed hybrid BiGRU deep learning model.

scholarly journals Self-Refine Learning For Data-Centric Text Classification

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Model Prediction ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Simple Method ◽  
Human Evaluation ◽  
Evaluation Accuracy

<div> <div> <p>In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and re-label their labels to the result of model prediction. We select the noisy data whose human label is not contained in the top-K model’s predictions. The model is trained on the origin dataset. The experiment result shows that our method works. For industry deep learning application, our method improve the text classification accuracy from 80.5% to 90.6% in dev dataset, and improve the human-evaluation accuracy from 83.2% to 90.5%.<br></p> </div> </div>

scholarly journals Self-Refine Learning For Data-Centric Text Classification

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Model Prediction ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Simple Method ◽  
Human Evaluation ◽  
Evaluation Accuracy

<div> <div> <p>In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and re-label their labels to the result of model prediction. We select the noisy data whose human label is not contained in the top-K model’s predictions. The model is trained on the origin dataset. The experiment result shows that our method works. For industry deep learning application, our method improve the text classification accuracy from 80.5% to 90.6% in dev dataset, and improve the human-evaluation accuracy from 83.2% to 90.5%.<br></p> </div> </div>

scholarly journals Self-Refine Learning For Data-Centric Text Classification

2021 ◽  
Author(s):  
Tong Guo
Keyword(s):  
Deep Learning ◽  
Text Classification ◽  
Model Prediction ◽  
Classification Accuracy ◽  
Noisy Data ◽  
Simple Method ◽  
Human Evaluation ◽  
Evaluation Accuracy

<div> <div> <p>In industry NLP application, our manually labeled data has a certain number of noisy data. We present a simple method to find the noisy data and re-label their labels to the result of model prediction. We select the noisy data whose human label is not contained in the top-K model’s predictions. The model is trained on the origin dataset. The experiment result shows that our method works. For industry deep learning application, our method improve the text classification accuracy from 80.5% to 90.6% in dev dataset, and improve the human-evaluation accuracy from 83.2% to 90.5%.<br></p> </div> </div>

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