Augmented Dataset Research Articles

Visual-based vehicle detection with adaptive oversampling

In this study, the class imbalance issue in vehicle detection was addressed. Specifically, certain classes such as Tow Truck were found to have significantly fewer samples compared to others such as normal trucks. This imbalance could be adversely impacted algorithm performance, favouring abundant classes over underrepresented ones. After thorough analysis, an adaptive dataset augmentation approach was proposed for the underrepresented classes. Evaluation was first performed on classic and state-of-the-art object detection methods. All experiments were undertaken on a tiny dataset called Multimedia University Diversity Dataset (MMUVD). The fastest training process and the highest mean average precision (mAP), which stood at 0.686 for mAP50 and 0.439 for mAP50-95, were demonstrated by You Only Look Once version 8 nano (YOLOv8n). By applying adaptive oversampling to the dataset and retesting it again on YOLOv8n, mAP50 was improved to 0.950 and mAP50-95 to 0.717, respectively. Notably, the contribution lay in identifying the optimal detection algorithm for vehicle detection, and the proposed adaptive oversampling method ensured consistent performance across all classes, enhancing the overall accuracy and reliability of the system.

Bluish veil detection and lesion classification using custom deep learnable layers with explainable artificial intelligence (XAI)

Melanoma, one of the deadliest types of skin cancer, accounts for thousands of fatalities globally. The bluish, blue-whitish, or blue-white veil (BWV) is a critical feature for diagnosing melanoma, yet research into detecting BWV in dermatological images is limited. This study utilizes a non-annotated skin lesion dataset, which is converted into an annotated dataset using a proposed imaging algorithm (color threshold techniques) on lesion patches based on color palettes. A Deep Convolutional Neural Network (DCNN) is designed and trained separately on three individual and combined dermoscopic datasets, using custom layers instead of standard activation function layers. The model is developed to categorize skin lesions based on the presence of BWV. The proposed DCNN demonstrates superior performance compared to the conventional BWV detection models across different datasets. The model achieves a testing accuracy of 85.71 % on the augmented PH2 dataset, 95.00 % on the augmented ISIC archive dataset, 95.05 % on the combined augmented (PH2+ISIC archive) dataset, and 90.00 % on the Derm7pt dataset. An explainable artificial intelligence (XAI) algorithm is subsequently applied to interpret the DCNN's decision-making process about the BWV detection. The proposed approach, coupled with XAI, significantly improves the detection of BWV in skin lesions, outperforming existing models and providing a robust tool for early melanoma diagnosis.

Improving skin cancer detection by Raman spectroscopy using convolutional neural networks and data augmentation.

Our previous studies have demonstrated that Raman spectroscopy could be used for skin cancer detection with good sensitivity and specificity. The objective of this study is to determine if skin cancer detection can be further improved by combining deep neural networks and Raman spectroscopy. Raman spectra of 731 skin lesions were included in this study, containing 340 cancerous and precancerous lesions (melanoma, basal cell carcinoma, squamous cell carcinoma and actinic keratosis) and 391 benign lesions (melanocytic nevus and seborrheic keratosis). One-dimensional convolutional neural networks (1D-CNN) were developed for Raman spectral classification. The stratified samples were divided randomly into training (70%), validation (10%) and test set (20%), and were repeated 56 times using parallel computing. Different data augmentation strategies were implemented for the training dataset, including added random noise, spectral shift, spectral combination and artificially synthesized Raman spectra using one-dimensional generative adversarial networks (1D-GAN). The area under the receiver operating characteristic curve (ROC AUC) was used as a measure of the diagnostic performance. Conventional machine learning approaches, including partial least squares for discriminant analysis (PLS-DA), principal component and linear discriminant analysis (PC-LDA), support vector machine (SVM), and logistic regression (LR) were evaluated for comparison with the same data splitting scheme as the 1D-CNN. The ROC AUC of the test dataset based on the original training spectra were 0.886±0.022 (1D-CNN), 0.870±0.028 (PLS-DA), 0.875±0.033 (PC-LDA), 0.864±0.027 (SVM), and 0.525±0.045 (LR), which were improved to 0.909±0.021 (1D-CNN), 0.899±0.022 (PLS-DA), 0.895±0.022 (PC-LDA), 0.901±0.020 (SVM), and 0.897±0.021 (LR) respectively after augmentation of the training dataset (p<0.0001, Wilcoxon test). Paired analyses of 1D-CNN with conventional machine learning approaches showed that 1D-CNN had a 1-3% improvement (p<0.001, Wilcoxon test). Data augmentation not only improved the performance of both deep neural networks and conventional machine learning techniques by 2-4%, but also improved the performance of the models on spectra with higher noise or spectral shifting. Convolutional neural networks slightly outperformed conventional machine learning approaches for skin cancer detection by Raman spectroscopy.

Maturity grading of jujube for industrial applications harnessing deep learning

PurposeTo develop and examine an efficient and reliable jujube grading model with reduced computational time, which could be utilized in the food processing and packaging industries to perform quick grading and pricing of jujube as well as for the other similar types of fruits.Design/methodology/approachThe whole process begins with manual analysis and collection of four jujube grades from the jujube tree, in addition to this jujube image acquisition was performed utilizing MVS which is further followed by image pre-processing and augmentation tasks. Eventually, classification models (i.e. proposed model, from scratch and pre-trained VGG16 and AlexNet) were trained and validated over the original and augmented datasets to discriminate the jujube into maturity grades.FindingsThe highest success rates reported over the original and augmented datasets were 97.53% (i.e. error of 2.47%) and 99.44% (i.e. error of 0.56%) respectively using Adam optimizer and a learning rate of 0.003.Research limitations/implicationsThe investigation relies upon a single view of the jujube image and the outer appearance of the jujube. In the future, multi-view image capturing system could be employed for the model training/validation.Practical implicationsDue to the vast functional derivatives of jujube, the identification of maturity grades of jujube is paramount in the fruit industry, functional food production industries and pharmaceutical industry. Therefore, the proposed model which is practically feasible and easy to implement could be utilized in such industries.Originality/valueThis research examines the performance of proposed CNN models for selected optimizer and learning rates for the grading of jujube maturity into four classes and compares them with the classical models to depict the sublime model in terms of accuracy, the number of parameters, epochs and computational time. After a thorough investigation of the models, it was discovered that the proposed model transcends both classical models in all aspects for both the original and augmented datasets utilizing Adam optimizer with learning rate of 0.003.

Hybrid brain tumor classification of histopathology hyperspectral images by linear unmixing and an ensemble of deep neural networks.

Hyperspectral imaging has demonstrated its potential to provide correlated spatial and spectral information of a sample by a non-contact and non-invasive technology. In the medical field, especially in histopathology, HSI has been applied for the classification and identification of diseased tissue and for the characterization of its morphological properties. In this work, we propose a hybrid scheme to classify non-tumor and tumor histological brain samples by hyperspectral imaging. The proposed approach is based on the identification of characteristic components in a hyperspectral image by linear unmixing, as a features engineering step, and the subsequent classification by a deep learning approach. For this last step, an ensemble of deep neural networks is evaluated by a cross-validation scheme on an augmented dataset and a transfer learning scheme. The proposed method can classify histological brain samples with an average accuracy of 88%, and reduced variability, computational cost, and inference times, which presents an advantage over methods in the state-of-the-art. Hence, the work demonstrates the potential of hybrid classification methodologies to achieve robust and reliable results by combining linear unmixing for features extraction and deep learning forclassification.

Deep learning with mixup augmentation for improved pore detection during additive manufacturing

In additive manufacturing (AM), process defects such as keyhole pores are difficult to anticipate, affecting the quality and integrity of the AM-produced materials. Hence, considerable efforts have aimed to predict these process defects by training machine learning (ML) models using passive measurements such as acoustic emissions. This work considered a dataset in which keyhole pores of a laser powder bed fusion (LPBF) experiment were identified using X-ray radiography and then registered both in space and time to acoustic measurements recorded during the LPBF experiment. Due to AM’s intrinsic process controls, where a pore-forming event is relatively rare, the acoustic datasets collected during monitoring include more non-pores than pores. In other words, the dataset for ML model development is imbalanced. Moreover, this imbalanced and sparse data phenomenon remains ubiquitous across many AM monitoring schemes since training data is nontrivial to collect. Hence, we propose a machine learning approach to improve this dataset imbalance and enhance the prediction accuracy of pore-labeled data. Specifically, we investigate how data augmentation helps predict pores and non-pores better. This imbalance is improved using recent advances in data augmentation called Mixup, a weak-supervised learning method. Convolutional neural networks (CNNs) are trained on original and augmented datasets, and an appreciable increase in performance is reported when testing on five different experimental trials. When ML models are trained on original and augmented datasets, they achieve an accuracy of 95% and 99% on test datasets, respectively. We also provide information on how dataset size affects model performance. Lastly, we investigate the optimal Mixup parameters for augmentation in the context of CNN performance.

Balancing act: Tackling organized retail fraud on e-commerce platforms with imbalanced learning text models

As online shopping expands rapidly, so does the prevalence of fraud, resulting in significant losses for retailers. According to the 2020 National Retail Federation (NRF) report, organized retail crime costs retailers nearly $800,000 per billion in sales, with an expected global annual increase of over fourteen percent. This paper introduces a text-based fraud detection framework to mitigate these losses efficiently. The framework comprises four key components: text preprocessing, representation, knowledge extraction via machine learning algorithms, and model evaluation. By integrating data augmentation techniques, the framework enhances classifier performance in detecting fraud. The proposed method, employing a combination of FastText and Random Forest classifiers, achieves an impressive F1 score of 0.833 and AUC score of 0.99 on an augmented dataset, surpassing conventional keyword-based models. Informed by best practices in fraud detection, this scalable framework promises a solution to combat the escalating fraud associated with the exponential growth of online shopping.

Mitigating Large Language Model Bias: Automated Dataset Augmentation and Prejudice Quantification

Despite the growing capabilities of large language models, concerns exist about the biases they develop. In this paper, we propose a novel, automated mechanism for debiasing through specified dataset augmentation in the lens of bias producers that can be useful in a variety of industries, especially ones that are “restricted” and have limited data. We consider that bias can occur due to intrinsic model architecture and dataset quality. The two aspects are evaluated using two different metrics we created. We show that our dataset augmentation algorithm reduces bias as measured by our metrics. Our code can be found on an online GitHub repository.

FEDM: a convolutional neural network based fertilised egg detection model

ABSTRACT 1. The production of goose eggs holds significant economic value on a global scale and the quality of fertilised eggs is crucial for the successful hatching and sustained development of the poultry industry. Developing a low-cost fertilised egg identification system that is suitable for large-scale testing is of great significance. However, existing methods are expensive and have high environmental detection requirements, which limit their promotion. 2. To address this issue, an improved object detection model called FEDM based on YOLOv5 is proposed, which has been shown to be outstanding among nine models. The main network of YOLOv5 is enhanced with the SENet attention mechanism to improve the feature selection capability. The C3_DCNv3 is introduced to enhance the detection ability of blood vessels in the fertilised eggs. The application of Dyhead significantly improved the representation capacity of the object detection head without any computational overhead. The loss function is replaced with MPDIoU to simplify the calculation process. 3. Experimental results from the augmented dataset showed that the average precision of the FEDM reached 96.7%, which is a 5.5% improvement compared to the YOLOv5s model. FEDM exhibited better detection performance on eggs from different shooting angles than the YOLOv5 algorithm and achieves high detection speed. 4. The FEDM secured significant advancement on the detection rate of the fourth day fertilised egg compared to the YOLOv5 algorithm. Based on this result, savings and space utilisation can be made, which has practical application value.

A Comparative Analysis of the Novel Conditional Deep Convolutional Neural Network Model, Using Conditional Deep Convolutional Generative Adversarial Network-Generated Synthetic and Augmented Brain Tumor Datasets for Image Classification.

Disease prediction is greatly challenged by the scarcity of datasets and privacy concerns associated with real medical data. An approach that stands out to circumvent this hurdle is the use of synthetic data generated using Generative Adversarial Networks (GANs). GANs can increase data volume while generating synthetic datasets that have no direct link to personal information. This study pioneers the use of GANs to create synthetic datasets and datasets augmented using traditional augmentation techniques for our binary classification task. The primary aim of this research was to evaluate the performance of our novel Conditional Deep Convolutional Neural Network (C-DCNN) model in classifying brain tumors by leveraging these augmented and synthetic datasets. We utilized advanced GAN models, including Conditional Deep Convolutional Generative Adversarial Network (DCGAN), to produce synthetic data that retained essential characteristics of the original datasets while ensuring privacy protection. Our C-DCNN model was trained on both augmented and synthetic datasets, and its performance was benchmarked against state-of-the-art models such as ResNet50, VGG16, VGG19, and InceptionV3. The evaluation metrics demonstrated that our C-DCNN model achieved accuracy, precision, recall, and F1 scores of 99% on both synthetic and augmented images, outperforming the comparative models. The findings of this study highlight the potential of using GAN-generated synthetic data in enhancing the training of machine learning models for medical image classification, particularly in scenarios with limited data available. This approach not only improves model accuracy but also addresses privacy concerns, making it a viable solution for real-world clinical applications in disease prediction and diagnosis.

A novel post-processing technique for correcting symmetric implant ambiguity in measuring total knee arthroplasty kinematics from single-plane fluoroscopy

Recent advancements in computer vision and machine learning enable autonomous measurement of total knee arthroplasty kinematics through single-plane fluoroscopy. However, symmetric components present challenges in optimization routines, causing “symmetry traps” and ambiguous poses. Achieving clinically robust kinematics measurement requires addressing this issue. We devised an algorithm that converts a “true” pose to its corresponding “symmetry trap” orientation. From a dataset of nearly 13,000 human supervised kinematics, this algorithm constructs an augmented dataset of “true” and “symmetry trap” kinematics, used to train eight classification machine learning algorithms. The outputs from the highest-performing algorithm classify kinematics sequences as ’obviously true’ or ’potentially ambiguous.’ We construct a spline through ’obviously true’ poses, and ’ambiguous’ poses are compared to the spline to determine correct orientation. The machine learning algorithms achieved 88–94% accuracy on our internal test set and 91–93% on our external test set. Applying our spline algorithm to kinematics sequences yielded 91.1% accuracy, 94% specificity, but 67% sensitivity. The accuracy of standard ML algorithms for implants within 5 degrees of a pure-lateral view was 71%, rising to 88% beyond 5 degrees. This pioneering study systematizes addressing model-image registration issues with symmetric tibial implants. High accuracy suggests potential use of ML algorithms to mitigate shape-ambiguity errors in pose measurements from single-plane fluoroscopy. Our results also suggest an imaging protocol for measuring kinematics that favors more oblique viewing angles, which could further disambiguate “true” and “symmetry trap” poses.

Enhancing deep learning predictive models with HAPPY (Hierarchically Abstracted rePeat unit of PolYmers) representation

We introduce HAPPY (Hierarchically ed rePeat unit of PolYmers), a string representation for polymers, designed to efficiently encapsulate essential polymer structure features for property prediction. HAPPY assigns single constituent elements to groups of sub-structures and employs grammatically complete and independent connectors between chemical linkages. Using a limited number of datapoints, we trained neural networks utilizing both HAPPY and conventional SMILES encoding of repeated unit structures and compared their performance in predicting five polymer properties: dielectric constant, glass transition temperature, thermal conductivity, solubility, and density. The results showed that the HAPPY-based network could achieve higher prediction R-squared score and two-fold faster training times. We further tested the robustness and versatility of HAPPY-based network with an augmented training dataset. Additionally, we present topo-HAPPY (Topological HAPPY), an extension that incorporates topological details of the constituent connectivity, leading to improved solubility and glass transition temperature prediction R-squared score.

Less is More: Stress Detection through Condensed Social Media Contents

In the digital age, social media has been a go-to platform for stress-related discussions, yielding valuable data to advance the understanding and detection of stress. Swift identification of stress indicators in these online conversations is essential in enabling immediate support and helping to avert subsequent severe mental and physical health issues, especially during global crises such as pandemics and conflicts. Detecting stress in social media posts automatically poses a formidable challenge. While techniques such as supervised Pretrained Language Models (PLMs) and zero-shot Large Language Models (LLMs) based classifiers have demonstrated significant performance, they exhibit limitations, especially on platforms like Reddit. For example, on Reddit, users tend to write lengthy, expressive posts, which causes these methods to often fail to consider the entire context, leading to incomplete or inaccurate assessments of a user's mental health or stress status. To overcome these limitations, we present a new approach to identifying and classifying stress-related discourse on social media. Our approach involves analyzing condensed versions of user posts, such as user-provided summaries or the "Too Long Didn’t Read" (TLDR) portion of the original post. We question whether these abridged texts can yield a more accurate classification of stress. In this paper, we make the following contributions. First, we investigate the relationship between the performance of the model's perceived textual context and the length of social media posts. Second, we present a novel approach to use the summarized texts for stress detection. We experiment with different classifiers to evaluate their performance on stress detection accuracy using summarized versus full-length posts. Furthermore, by examining the emotional and linguistic features of the original posts and their summaries, we suggest improvements to current state-of-the-art LLM-based stress classifier prompts, thereby enhancing stress detection capabilities. Finally, when user summaries are absent, we synthetically generate meaningful user post summaries by incorporating the power of LLMs. Our results show that the stress detection performance deteriorates for longer posts, and utilizing the TLDR and summaries improves classification outcomes. We also provide augmented datasets containing human and AI-generated summaries for future research in stress detection on social media.

Oversampling techniques for imbalanced data in regression

Our study addresses the challenge of imbalanced regression data in Machine Learning (ML) by introducing tailored methods for different data structures. We adapt K-Nearest Neighbor Oversampling-Regression (KNNOR-Reg), originally for imbalanced classification, to address imbalanced regression in low population datasets, evolving to KNNOR-Deep Regression (KNNOR-DeepReg) for high-population datasets. For tabular data, we also present the Auto-Inflater neural network, utilizing an exponential loss function for Autoencoders. For image datasets, we employ Multi-Level Autoencoders, consisting of Convolutional and Fully Connected Autoencoders. For such high-dimension data our approach outperforms the Synthetic Minority Oversampling Technique for Regression (SMOTER) algorithm for the IMDB-WIKI and AgeDB image datasets. For tabular data we conducted a comprehensive experiment using various models trained on both augmented and non-augmented datasets, followed by performance comparisons on test data. The outcomes revealed a positive impact of data augmentation, with a success rate of 83.75% for Light Gradient Boosting Method (LightGBM) and 71.57% for the 18 other regressors employed in the study. This success rate is determined by the frequency of instances where models performed better when augmented data was used compared to instances with no augmentation. Access to the comparative code can be found in GitHub.

Robust Forest Fire Detection Method for Surveillance Systems Based on You Only Look Once Version 8 and Transfer Learning Approaches

Forest fires have emerged as a significant global concern, exacerbated by both global warming and the expanding human population. Several adverse outcomes can result from this, including climatic shifts and greenhouse effects. The ramifications of fire incidents extend widely, impacting human communities, financial resources, the natural environment, and global warming. Therefore, timely fire detection is essential for quick and effective response and not to endanger forest resources, animal life, and the human economy. This study introduces a forest fire detection approach utilizing transfer learning with the YOLOv8 (You Only Look Once version 8) pretraining model and the TranSDet model, which integrates an improved deep learning algorithm. Transfer Learning based on pre-trained YoloV8 enhances a fast and accurate object detection aggregate with the TranSDet structure to detect small fires. Furthermore, to train the model, we collected 5200 images and performed augmentation techniques for data, such as rotation, scaling, and changing due and saturation. Small fires can be detected from a distance by our suggested model both during the day and at night. Objects with similarities can lead to false predictions. However, the dataset augmentation technique reduces the feasibility. The experimental results prove that our proposed model can successfully achieve 98% accuracy to minimize catastrophic incidents. In recent years, the advancement of deep learning techniques has enhanced safety and secure environments. Lastly, we conducted a comparative analysis of our method’s performance based on widely used evaluation metrics to validate the achieved results.

Enhancing Detection of Remotely-Sensed Floating Objects via Data Augmentation for Maritime SAR

A figure of 33,000 search and rescue (SAR) incidents were responded to by the UK’s HM Coastguard in 2020, and over 1322 rescue missions were conducted by SAR helicopters during that year. Combined with Unmanned Aerial Vehicles (UAVs), artificial intelligence, and computer vision, SAR operations can be revolutionized through enabling rescuers to expand ground coverage with improved detection accuracy whilst reducing costs and personal injury risks. However, detecting small objects is one of the significant challenges associated with using computer vision on UAVs. Several approaches have been proposed for improving small object detection, including data augmentation techniques like replication and variation of image sizes, but their suitability for SAR application characteristics remains questionable. To address these issues, this paper evaluates four float detection algorithms against the baseline and augmented datasets to improve float detection for maritime SAR. Results demonstrated that YOLOv8 and YOLOv5 outperformed the others in which F1 scores ranged from 82.9 to 95.3%, with an enhancement range of 0.1–29.2%. These models were both of low complexity and capable of real-time response.

Enhancing a Deep Learning Model for the Steam Reforming Process Using Data Augmentation Techniques

Methane steam reforming is the foremost method for hydrogen production, and it has been studied through experiments and diverse computational models to enhance its energy efficiency. This study focuses on employing an artificial neural network as a model of the methane steam reforming process. The proposed data-driven model predicts the output mixture’s composition based on reactor operating conditions, such as the temperature, steam-to-methane ratio, nitrogen-to-methane ratio, methane flow, and nickel catalyst mass. The network, a feedforward type, underwent training with a comprehensive dataset augmentation strategy that augments the primary experimental dataset through interpolation and theoretical simulations of the process, ensuring a robust model training phase. Additionally, it introduces weights to evaluate the relative significance of different data categories (experimental, interpolated, and theoretical) within the dataset. The optimal artificial neural network architecture was determined by evaluating various configurations, with the aim of minimizing the mean squared error (0.00022) and maximizing the Pearson correlation coefficient (0.97) and Spearman correlation coefficient (1.00).

TrG2P: A transfer-learning-based tool integrating multi-trait data for accurate prediction of crop yield

Yield prediction is the primary goal of genomic selection (GS)-assisted crop breeding. Because yield is a complex quantitative trait, making predictions from genotypic data is challenging. Transfer learning can produce an effective model for a target task by leveraging knowledge from a different, but related, source domain and is considered a great potential method for improving yield prediction by integrating multi-trait data. However, it has not previously been applied to genotype-to-phenotype prediction owing to the lack of an efficient implementation framework. We therefore developed TrG2P, a transfer-learning-based framework. TrG2P first employs convolutional neural networks (CNN) to train models using non-yield-trait phenotypic and genotypic data, thus obtaining pre-trained models. Subsequently, the convolutional layer parameters from these pre-trained models are transferred to the yield prediction task, and the fully connected layers are retrained, thus obtaining fine-tuned models. Finally, the convolutional layer and the first fully connected layer of the fine-tuned models are fused, and the last fully connected layer is trained to enhance prediction performance. We applied TrG2P to five sets of genotypic and phenotypic data from maize (Zea mays), rice (Oryza sativa), and wheat (Triticum aestivum) and compared its model precision to that of seven other popular GS tools: ridge regression best linear unbiased prediction (rrBLUP), random forest, support vector regression, light gradient boosting machine (LightGBM), CNN, DeepGS, and deep neural network for genomic prediction (DNNGP). TrG2P improved the accuracy of yield prediction by 39.9%, 6.8%, and 1.8% in rice, maize, and wheat, respectively, compared with predictions generated by the best-performing comparison model. Our work therefore demonstrates that transfer learning is an effective strategy for improving yield prediction by integrating information from non-yield-trait data. We attribute its enhanced prediction accuracy to the valuable information available from traits associated with yield and to training dataset augmentation. The Python implementation of TrG2P is available at https://github.com/lijinlong1991/TrG2P. The web-based tool is available at http://trg2p.ebreed.cn:81.

Improving Time Series Regression Model Accuracy via Systematic Training Dataset Augmentation and Sampling

This study addresses a significant gap in the field of time series regression modeling by highlighting the central role of data augmentation in improving model accuracy. The primary objective is to present a detailed methodology for systematic sampling of training datasets through data augmentation to improve the accuracy of time series regression models. Therefore, different augmentation techniques are compared to evaluate their impact on model accuracy across different datasets and model architectures. In addition, this research highlights the need for a standardized approach to creating training datasets using multiple augmentation methods. The lack of a clear framework hinders the easy integration of data augmentation into time series regression pipelines. Our systematic methodology promotes model accuracy while providing a robust foundation for practitioners to seamlessly integrate data augmentation into their modeling practices. The effectiveness of our approach is demonstrated using process data from two milling machines. Experiments show that the optimized training dataset improves the generalization ability of machine learning models in 86.67% of the evaluated scenarios. However, the prediction accuracy of models trained on a sufficient dataset remains largely unaffected. Based on these results, sophisticated sampling strategies such as Quadratic Weighting of multiple augmentation approaches may be beneficial.

Data augmentation and transfer learning for cross-lingual Named Entity Recognition in the biomedical domain

AbstractGiven the increase in production of data for the biomedical field and the unstoppable growth of the internet, the need for Information Extraction (IE) techniques has skyrocketed. Named Entity Recognition (NER) is one of such IE tasks useful for professionals in different areas. There are several settings where biomedical NER is needed, for instance, extraction and analysis of biomedical literature, relation extraction, organisation of biomedical documents, and knowledge-base completion. However, the computational treatment of entities in the biomedical domain has faced a number of challenges including its high cost of annotation, ambiguity, and lack of biomedical NER datasets in languages other than English. These difficulties have hampered data development, affecting both the domain itself and its multilingual coverage. The purpose of this study is to overcome the scarcity of biomedical data for NER in Spanish, for which only two datasets exist, by developing a robust bilingual NER model. Inspired by back-translation, this paper leverages the progress in Neural Machine Translation (NMT) to create a synthetic version of the Colorado Richly Annotated Full-Text (CRAFT) dataset in Spanish. Additionally, a new CRAFT dataset is constructed by replacing 20% of the entities in the original dataset generating a new augmented dataset. We evaluate two training methods: concatenation of datasets and continuous training to assess the transfer learning capabilities of transformers using the newly obtained datasets. The best performing NER system in the development set achieved an F-1 score of 86.39%. The novel methodology proposed in this paper presents the first bilingual NER system and it has the potential to improve applications across under-resourced languages.