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A BERT-Based Automatic Scorino Model of Korean Language Learners Essay

JOURNAL OF INFORMATION PROCESSING SYSTEMS 2022年第2期18卷 282-291页

作者： Lee, Jung Hee Park, Ji Su Shon, Jin Gon Kyung Hee Univ Dept Korean Language Educ Language 2 Seoul South Korea Jeonju Univ Dept Comp Sci & Engn Jeonju South Korea Korea Natl Open Univ Dept Comp Sci Seoul South Korea

This research applies a pre-trained bidirectional encoder representations from transformers (BERT) handwriting recognition model to predict foreign Korean-language learners' writing scores. A corpus of 586 answers to midterm and final exams written by foreign learners at the Intermediate 1 level was acquired and used for pre-training, resulting in consistent performance, even with small datasets. The test data were pre-processed and fine-tuned, and the results were calculated in the form of a score prediction. The difference between the prediction and actual score was then calculated. An accuracy of 95.8% was demonstrated, indicating that the prediction results were strong overall;hence, the tool is suitable for the automatic scoring of Korean written test answers, including grammatical errors, written by foreigners. These results are particularly meaningful in that the data included written language text produced by foreign learners, not native speakers.

关键词： Automatic Writing Scoring bidirectional encoder representations from transformers Korean as a Foreign Language Natural Language Processing

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HEalthRecordBERT (HERBERT): Leveraging transformers on Electronic Health Records for Chronic Kidney Disease Risk Stratification

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ACM TRANSACTIONS ON COMPUTING FOR HEALTHCARE 2024年第3期5卷 1-18页

作者： Moore, Alex Orset, Bastien Yassaee, Arrash Irving, Benjamin Morelli, Davide Huma Therapeut Ltd London England Imperial Coll London England Univ Oxford Oxford England

Risk stratification is an essential tool in the fight against many diseases, including chronic kidney disease. Recent work has focused on applying techniques from machine learning and leveraging the information contained in a patient's electronic health record (EHR). Irregular intervals between data entries and the large number of variables tracked in EHR datasets can make them challenging to work with. Many of the difficulties associated with these datasets can be overcome by using large language models, such as bidirectional encoder representations from transformers (BERT). Previous attempts to apply BERT to EHR for risk stratification have shown promise. In this work we propose HERBERT, a novel application of BERT to EHR data. We identify two key areas where BERT models must be modified to adapt them to EHR data, namely: the embedding layer and the pretraining task. We show how changes to these can lead to improved performance, relative to the previous state of the art. We evaluate our model by predicting the transition of chronic kidney disease patients to end stage renal disease. The strong performance of our model justifies our architectural changes and suggests that large language models could play an important role in future renal risk stratification. CCS Concepts: center dot Computing methodologies - Machine learning;center dot Applied computing - Health care information systems

关键词： bidirectional encoder representations from transformers electronic health records machine learning medical risk stratification

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HACNN: hierarchical attention convolutional neural network for fake review detection

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SOCIAL NETWORK ANALYSIS AND MINING 2024年第1期14卷 223页

作者： Venkatesh, Bhoompally Yadav, B. V. Ramnaresh Jawaharlal Nehru Technol Univ Hyderabad Dept CSE Hyderabad 500085 Telangana India

Fake news is intentionally misleading and is often spread through social media platforms like Facebook and Twitter. The spread of false information on these platforms is a growing problem that needs to be addressed. Fake reviews are also an issue, as they mislead consumers and can harm online review systems. Therefore, it is essential to distinguish between real and fake reviews on online stores to save customers from fraud. Most existing methods for detecting fake reviews are not accurate enough due to a lack of labelled data and reliance on single features. The major objective of this research is to introduce a hierarchical attention network-convolutional neural network (HACNN) for fake review detection. This HACNN is formed by the amalgamation of deep convolutional neural network, and the hierarchical attention network. Here, the HACNN model is implemented as follows. Firstly, the input review data taken from a database is applied to the bidirectional encoder representations from transformers tokenizer. After that, the feature extraction, data augmentation, and feature pruning are accomplished. Lastly, the pruned features are subjected to the HACNN for fake review detection. Furthermore, the HACNN is tuned by employing the adam archery algorithm. The AAA is developed by the combination of the archery algorithm, and adam optimization. The performance of HACNN is measured by four performance measures, such as precision, accuracy, f-measure, and recall. The proposed method has superior values, like 0.929, 0.912, 0.932, and 0.936 for precision, accuracy, f-measure, and recall, respectively.

关键词： Hierarchical attention network-convolutional neural network Hierarchical attention network Deep convolutional neural network Adam archery algorithm bidirectional encoder representations from transformers

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User Dynamics and Thematic Exploration in r/Depression During the COVID-19 Pandemic: Insights from Overlapping r/SuicideWatch Users

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JOURNAL OF MEDICAL INTERNET RESEARCH 2024年 26卷 e53968页

作者： Zhu, Jianfeng Jin, Ruoming Kenne, Deric R. Phan, Nhathai Ku, Wei-Shinn Kent State Univ Dept Comp Sci 800 E Summit St Kent OH 44242 USA Kent State Univ Coll Publ Hlth Ctr Publ Policy & Hlth Kent OH USA New Jersey Inst Technol Dept Data Sci Newark NJ USA Auburn Univ Dept Comp Sci & Software Engn Auburn AL USA

Background: In 2023, the United States experienced its highest- recorded number of suicides, exceeding 50,000 deaths. In the realm of psychiatric disorders, major depressive disorder stands out as the most common issue, affecting 15% to 17% of the population and carrying a notable suicide risk of approximately 15%. However, not everyone with depression has suicidal thoughts. While "suicidal depression" is not a clinical diagnosis, it may be observed in daily life, emphasizing the need for awareness. Objective: This study aims to examine the dynamics, emotional tones, and topics discussed in posts within the r/Depression subreddit, with a specific focus on users who had also engaged in the r/SuicideWatch community. The objective was to use natural language processing techniques and models to better understand the complexities of depression among users with potential suicide ideation, with the goal of improving intervention and prevention strategies for suicide. Methods: Archived posts were extracted from the r/Depression and r/SuicideWatch Reddit communities in English spanning from 2019 to 2022, resulting in a final data set of over 150,000 posts contributed by approximately 25,000 unique overlapping users. A broad and comprehensive mix of methods was conducted on these posts, including trend and survival analysis, to explore the dynamic of users in the 2 subreddits. The BERT family of models extracted features from data for sentiment and thematic analysis. Results: On August 16, 2020, the post count in r/SuicideWatch surpassed that of r/Depression. The transition from r/Depression to r/SuicideWatch in 2020 was the shortest, lasting only 26 days. Sadness emerged as the most prevalent emotion among overlapping users in the r/Depression community. In addition, physical activity changes, negative self-view, and suicidal thoughts were identified as the most common depression symptoms, all showing strong positive correlations with the emotion tone of disappointment. Furt

关键词： reddit natural language processing NLP suicidal ideation SI online communities depression symptoms COVID-19 pandemic bidirectional encoder representations from transformers BERT r/SuicideWatch r/Depression

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MDL-CPI: Multi-view deep learning model for compound-protein interaction prediction

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METHODS 2022年 204卷 418-427页

作者： Wei, Lesong Long, Wentao Wei, Leyi Hainan Normal Univ Sch Math & Stat Hainan Peoples R China Univ Tsukuba Dept Comp Sci Tsukuba 3058577 Japan Shandong Univ Sch Software Jinan Peoples R China Shandong Univ Joint SDU NTU Ctr Artificial Intelligence Res C FA Jinan Peoples R China

Elucidating the mechanisms of Compound-Protein Interactions (CPIs) plays an essential role in drug discovery and development. Many computational efforts have been done to accelerate the development of this field. However, the current predictive performance is still not satisfactory, and existing methods consider only protein and compound features, ignoring their interactive information. In this study, we propose a multi-view deep learning method named MDL-CPI for CPI prediction. To sufficiently extract discriminative information, we introduce a hybrid architecture that leverages BERT (bidirectional encoder representations from transformers) and CNN (Convolutional Neural Network) to extract protein features from a sequential perspective, use the GNN (Graph Neural Networks) to extract compound features from a structural perspective, and generate a unified feature space by using AE2 (Autoencoder in Autoencoder Networks) network to learn the interactive information between BERT-CNN and Graph embeddings. Comparative results on benchmark datasets show that our proposed method exhibits better performance compared to existing CPI prediction methods, demonstrating the strong predictive ability of our model. Importantly, we demonstrate that the learned interactive information between compounds and proteins is critical to improve predictive performance. We release our source code and dataset at: https://***/Longwt123/MDL-CPI.

关键词： Compound-protein interaction Multi-view learning Graph Neural Networks bidirectional encoder representations from transformers

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Context-dependent model for spam detection on social networks

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SN APPLIED SCIENCES 2020年第9期2卷 1-8页

作者： Ghanem, Razan Erbay, Hasan KiriKkale Univ Dept Comp Engn Kirikkale Turkey Univ Turkish Aeronaut Assoc Dept Comp Engn Ankara Turkey

Social media platforms are getting an important communication medium in our daily life, and their increasing popularity makes them an ideal platform for spammers to spread spam messages, known as spam problems. Moreover, messages on social media are vague and messy, so a good representation of the text may be the first step to address spam problem. While traditional weighting methods suffer from both high dimensionality and high sparsity problems, traditional word embedding methods suffer from context independence and out of vocabulary problems. To overcome these problems, in this study, we propose a novel architecture based on a context-dependent representation of text using the BERT model. The model was tested using the Twitter dataset, and experimental results show that the proposed method outperforms traditional weighting methods, traditional word embedding based methods as well as the existing state of the art methods used to detect spam on the twitter platform.

关键词： Spam detection Word embedding bidirectional encoder representations from transformers

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Natural language processing: fast forwarding research to the "good stuff" Natural language processing for nutrition

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AMERICAN JOURNAL OF CLINICAL NUTRITION 2023年第3期117卷 449-450页

作者： Lindquist, Joseph M. US Mil Acad Dept Math Sci West Point NY 10996 USA

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Self-adaptive Education Resource Allocation using BERT Model 24

Self-adaptive Education Resource Allocation using BERT Model

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Wen, Yi Wuhan Business Univ Wuhan 430056 Hubei Peoples R China

ISBN: (纸本)9798400718144

The balanced allocation of educational resources plays an important role in improving the quality of education and promoting educational equity. The current configuration models are difficult to understand resource information features and have limitations in automatic learning of features and resource classification. To improve the effectiveness of resource allocation and enhance the level of educational development, this article combines the BERT (bidirectional encoder representations from transformers) model to study self-adaptive allocation of educational resources. By using a brand new pre-trained language representation, the features of educational resources are extracted and interpreted, and the allocation scheme is optimized using a fitness function defined by heuristic algorithms. In the experimental analysis, this article uses the SVM (Support Vector Machine) model and CNN (Convolutional Neural Network) model as references to verify the application effect of the BERT model. The results showed that in the analysis of configuration effects, after training and optimizing the BERT model, the student-teacher ratio of various universities achieved significant improvement, with specific proportion values below 10.00. It can be concluded that self-adaptive allocation of educational resources based on the BERT model can help to achieve balanced development of educational resources and promote high-quality development of education.

关键词： Allocation of educational resources bidirectional encoder representations from transformers Self-adaptive mechanism Heuristic algorithm Resource classification

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Design of Multimodal Retrieval Model for Translation Domain Based on BERT 24

Design of Multimodal Retrieval Model for Translation Domain ...

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International Conference on Machine Intelligence and Digital Applications (MIDA)

作者： Sheng, Xia Hubei Three Gorges Polytech Yichang 443000 Hubei Peoples R China

ISBN: (纸本)9798400718144

There are a large number of languages in the world, each with its unique grammar, vocabulary, and cultural characteristics, and there are ambiguities in language. The same word may have different meanings in different contexts, making cross language translation more complex. To address these issues, this article designed a multimodal retrieval model based on BERT (bidirectional encoder representations from transformers), with the specific process as follows. Firstly, BERT was used as the text encoder and ResNet (Residual Network) was used as the image encoder. Then, a modal fusion algorithm was designed to establish the model and train the model. Finally, through two comparative experiments, three other multimodal machine translation models were applied as the benchmark methods for the three comparisons in this article. After training, the accuracy of the model in translation retrieval in this article exceeded 0.9 in 100 iterations of training. Experiments show that the multimodal retrieval model developed in this paper can accurately translate the elements and events in the pictures, and the detection results are similar to the semantics of the text annotations that come with the pictures. On the contrary, the other three models are able to translate only a part of the elements or events in the pictures. The model in this paper achieves significant performance results in retrieval tasks in the translation domain, verifying its effectiveness and usefulness in cross-modal retrieval.

关键词： bidirectional encoder representations from transformers Residual network multimodal retrieval model Machine translation Language ambiguity

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Enhanced Unsupervised Data Augmentation for Emergency Events Detection and Classification 33

Enhanced Unsupervised Data Augmentation for Emergency Events...

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33rd Chinese Control and Decision Conference (CCDC)

作者： Liu, Xiaomeng Long, Fei Huang, Kun Ling, Qiang Chinaso Inc Beijing 100077 Peoples R China Xinhua News Agcy State Key Lab Media Convergence Prod Technol & Sy Beijing 100077 Peoples R China Univ Sci & Technol China Dept Automat Hefei 230026 Peoples R China

ISBN: (纸本)9781665440899

We propose to jointly detect and classify emergency events using a multi-class text classifier, which is a typical deep learning architecture with transformer modules and particularly employs bidirectional encoder representations from transformers(BERT). Deep learning requires a large number of labeled data to work. Meanwhile, deep learning often implements the semi-supervised learning(SSL) method, which is able to use massive unlabeled data to improve performance of supervised deep learning. As an effective SSL variant, unsupervised data augmentation (UDA) focuses on data augmentation techniques to improve the performance of deep learning. We present an enhanced version of UDA(EUDA) by mixing more data augmentation strategies and using a problem related prefilter. Our EUDA targets at emergency event detection and classification. Considering that emergency events always have time and location elements, text can be filtered based on this semantic feature. We propose to use semantic feature aided enhanced unsupervised data augmentation to solve the concerned problem. Empirical studies on the dataset prepared for the task validates that the proposed EUDA can achieve significantly better performance than supervised learning with a limited size of labeled data. Experiments are also carried out on a text classification task, which confirms that EUDA improves performance for BERT neural network.

关键词： Semi-supervised Learning bidirectional encoder representations from transformers Unsupervised Data Augmentation

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