检索结果-内蒙古大学图书馆

Using of transformers Models for Text Classification to Mobile Educational Applications

IEEE LATIN AMERICA TRANSACTIONS 2023年第6期21卷 730-736页

作者： Garrido, Anabel Pilicita Arias, Enrique Barra Univ Politecn Madrid Madrid Spain

In Q2 2022, educational apps were the second most popular category on the Google Play Store, accounting for 10.47% of the apps available worldwide. This work explores the application of five BERT-based pre-trained models with the transformers architecture to classify mobile educational applications. These five models are according to the knowledge field: bert-base-cased, bert-base-uncased, roberta-base, albert-base-v2 and distilbert-base-uncased. This study uses a dataset with educational apps of Google Play, this dataset was enriched with description and category because it lacked this information. In all models, a tokenizer and fine-tuning works were applied for training in the classification task. After training the data, the testing phase was performed in which the models had to go through four training epochs to obtain better results: roberta-base with 81% accuracy, bert-base-uncased with 79% accuracy, bert-base-cased obtained 80% accuracy, albert-base-v2 obtained 78% accuracy and distilbert-base-uncased obtained 76% accuracy.

关键词： Bit error rate transformers Internet Training Text categorization Recurrent neural networks IEEE transactions Natural Language Processing Multiclass Text Classification bidirectional encoder representations from transformers

来源：评论

学校读者我要写书评

暂无评论

DHMFRD - TER: a deep hybrid model for fake review detection incorporating review texts, emotions, and ratings

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第2期83卷 4533-4549页

作者： Duma, Ramadhani Ally Niu, Zhendong Nyamawe, Ally Tchaye-Kondi, Jude Chambua, James Yusuf, Abdulganiyu Abdu Beijing Inst Technol Sch Comp Sci & Technol Beijing Peoples R China Univ Dodoma Coll Informat & Virtual Educ Dodoma Tanzania Univ Dar Es Salaam Comp Sci & Engn Dept Dar Es Salaam Tanzania

Recently, there has been an increasing reward to manipulate product/ service reviews, mostly profit-driven, since positive reviews infer high business returns and vice versa. To combat this issue, experts in industry and researchers recently attempted integrating multi-aspect (reviewer- and review-centric) data features. However, the emotions hidden in the review, the semantic meaning of the review, and data heterogeneity still deserve more study as they are essential indicators of fake content. This study proposed a Deep Hybrid Model for Fake Review Detection incorporating review Texts, Emotions, and Ratings (DHMFRD - TER). Initially, it computes contextualized review text vectors and extraction of emotion indicators representations. Then, the model learns the representation to extract higher-level review features. Finally, contextualized word vectors, ratings, and emotions are concatenated;such a multidimensional feature representation is used to classify reviews. Extensive experiments on three publicly available datasets demonstrate that DHMFRD-TER significantly outperforms state-of-the-art baseline approaches, achieving an accuracy of 0.988, 0.987, and 0.994 in Amazon, Yelp CHI, and OSF datasets, respectively.

关键词： Fake reviews Ratings Emotions Convolution neural network Long short-term memory bidirectional encoder representations from transformers

来源：评论

学校读者我要写书评

暂无评论

Large Language Models for Mental Health Applications:Systematic Review

引用

JMIR MENTAL HEALTH 2024年 11卷 e57400页

作者： Guo, Zhijun Lai, Alvina Thygesen, Johan H. Farrington, Joseph Keen, Thomas Li, Kezhi UCL Inst Hlth Informat 222 Euston Rd London NW1 2DA England UCL Great Ormond St Inst Child Hlth London England

Background: Large language models (LLMs) are advanced artificial neural networks trained on extensive datasets to accurately understand and generate natural language. While they have received much attention and demonstrated potential in digital health, their application in mental health, particularly in clinical settings, has generated considerable debate. Objective: This systematic review aims to critically assess the use of LLMs in mental health, specifically focusing on theirapplicability and efficacy in early screening, digital interventions, and clinical settings. By systematically collating and assessingthe evidence from current studies, our work analyzes models, methodologies, data sources, and outcomes, thereby highlightingthe potential of LLMs in mental health, the challenges they present, and the prospects for their clinical use. Methods: Adhering to the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines, thisreview searched 5 open-access databases: MEDLINE (accessed by PubMed), IEEE Xplore, Scopus, JMIR, and ACM DigitalLibrary. Keywords used were (mental healthOR mental illnessOR mental disorderOR psychiatry) AND (large languagemodels). This study included articles published between January 1, 2017, and April 30, 2024, and excluded articles published inlanguages other than English. Results: In total, 40 articles were evaluated, including 15 (38%) articles on mental health conditions and suicidal ideationdetection through text analysis, 7 (18%) on the use of LLMs as mental health conversational agents, and 18 (45%) on otherapplications and evaluations of LLMs in mental health. LLMs show good effectiveness in detecting mental health issues andproviding accessible, destigmatized eHealth services. However, assessments also indicate that the current risks associated withclinical use might surpass their benefits. These risks include inconsistencies in generated text;the production of hallucinations;and the absence of a compre

关键词： large language models mental health digital health care ChatGPT bidirectional encoder representations from transformers BERT

来源：评论

学校读者我要写书评

暂无评论

The Most Effective Interventions for Classification ModelDevelopment to Predict Chat Outcomes Based on theConversation Content in Online Suicide Prevention Chats:MachineLearning Approach

引用

JMIR MENTAL HEALTH 2024年 11卷 e57362页

作者： Salmi, Salim Merel, Saskia Gilisse, Renske van der Mei, Rob Bhulai, Sandjai 113 Suicide Prevent Res Dept Paasheuvelweg 25 NL-1105 BP Amsterdam Netherlands Ctr Wiskunde & Informat Dept Stochast Amsterdam Netherlands Vrije Univ Amsterdam Dept Math Amsterdam Netherlands

Background: For the provision of optimal care in a suicide prevention helpline, it is important to know what contributes topositive or negative effects on help seekers. Helplines can often be contacted through text-based chat services, which producelarge amounts of text data for use in large-scale analysis. Objective: We trained a machine learning classification model to predict chat outcomes based on the content of the chatconversations in suicide helplines and identified the counsellor utterances that had the most impact on its outputs. Methods: from August 2021 until January 2023, help seekers (N=6903) scored themselves on factors known to be associatedwith suicidality (eg, hopelessness, feeling entrapped, will to live) before and after a chat conversation with the suicide preventionhelpline in the Netherlands (113 Suicide Prevention). Machine learning text analysis was used to predict help seeker scores onthese factors. Using 2 approaches for interpreting machine learning models, we identified text messages from helpers in a chatthat contributed the most to the prediction of the model. Results: According to the machine learning model, helpers'positive affirmations and expressing involvement contributed toimproved scores of the help seekers. Use of macros and ending the chat prematurely due to the help seeker being in an unsafesituation had negative effects on help seekers. Conclusions: This study reveals insights for improving helpline chats, emphasizing the value of an evocative style with questions,positive affirmations, and practical advice. It also underscores the potential of machine learning in helpline chat analysi

关键词： suicide suicidality suicide prevention helpline suicide helpline classification interpretable AI explainable AI conversations BERT bidirectional encoder representations from transformers machine learning artificial intelligence large language models LLM natural language processing

来源：评论

学校读者我要写书评

暂无评论

G-BERT: An Efficient Method for Identifying Hate Speech in Bengali Texts on Social Media

引用

IEEE ACCESS 2023年 11卷 79697-79709页

作者： Keya, Ashfia Jannat Kabir, Md. Mohsin Shammey, Nusrat Jahan Mridha, M. F. Islam, Md. Rashedul Watanobe, Yutaka Bangladesh Univ Business & Technol Dept Comp Sci & Engn Dhaka 1216 Bangladesh Univ Girona Super Polytech Sch Girona 17071 Spain Amer Int Univ Bangladesh Dept Comp Sci & Engn Dhaka 1229 Bangladesh Univ Asia Pacific Dept Comp Sci & Engn Dhaka 1205 Bangladesh Univ Aizu Sch Comp Sci & Engn Aizu Wakamatsu 9658580 Japan

The rapid increase in Internet users has increased online concerns such as hate speech, abusive texts, and harassment. In Bangladesh, hate text in Bengali is frequently used on various social media platforms to condemn and abuse individuals. However, Research on recognizing hate speech in Bengali texts is lacking. The pervasive negative impact of hate speech on individuals' well-being and the urgent need for effective measures to address hate speech in Bengali texts have created a significant research gap in the Bengali hate speech detection field. This study suggests a technique for identifying hate speech in Bengali social media posts that may harm individuals' sentiments. Our approach utilizes the bidirectional encoder representations from transformers (BERT) architecture to extract Bengali text properties, whereas hate speech is categorized using a Gated Recurrent Units (GRU) model with a Softmax activation function. We propose a new model, G-BERT, that combines both models. We compared our model's performance with several other algorithms and achieved an accuracy, precision, recall, and F1-score of 95.56%, 95.07%, 93.63%, and 92.15%, respectively. Our proposed model outperformed all other classification algorithms tested. Our findings show that the strategy we have suggested is successful in locating hate speech in Bengali texts posted on social media platforms, which can aid in mitigating online hate speech and promoting a more respectful online environment.

关键词： bidirectional encoder representations from transformers deep learning hate speech gated recurrent unit social platform

来源：评论

学校读者我要写书评

暂无评论

TRCaptionNet: A novel and accurate deep Turkish image captioning model with vision transformer based image encoders and deep linguistic text decoders

引用

TURKISH JOURNAL OF ELECTRICAL ENGINEERING AND COMPUTER SCIENCES 2023年第6期31卷 1079-1098页

作者： Yildiz, Serdar Memis, Abbas Varli, Songul Yildiz Tech Univ Fac Elect & Elect Engn Dept Comp Engn Istanbul Turkiye Hlth Inst Turkiye Istanbul Turkiye TUBITAK BILGEM Kocaeli Turkiye

Image captioning is known as a fundamental computer vision task aiming to figure out and describe what is happening in an image or image region. Through an image captioning process, it is ensured to describe and define the actions and the relations of the objects within the images. In this manner, the contents of the images can be understood and interpreted automatically by visual computing systems. In this paper, we proposed the TRCaptionNet a novel deep learning-based Turkish image captioning (TIC) model for the automatic generation of Turkish captions. The model we propose essentially consists of a basic image encoder, a feature projection module based on vision transformers, and a text decoder. In the first stage, the system encodes the input images via the CLIP (contrastive language-image pretraining) image encoder. The CLIP image features are then passed through a vision transformer and the final image features to be linked with the textual features are obtained. In the last stage, a deep text decoder exploiting a BERT (bidirectional encoder representations from transformers) based model is used to generate the image cations. Furthermore, unlike the related works, a natural language-based linguistic model called NLLB (No Language Left Behind) was employed to produce Turkish captions from the original English captions. Extensive performance evaluation studies were carried out and widely known image captioning quantification metrics such as BLEU, METEOR, ROUGE-L, and CIDEr were measured for the proposed model. Within the scope of the experiments, quite successful results were observed on MS COCO and Flickr30K datasets, two known and prominent datasets in this field. As a result of the comparative performance analysis by taking the existing reports in the current literature on TIC into consideration, it was witnessed that the proposed model has superior performance and outperforms the related works on TIC so far. Project details and demo links of TRCaptionNet wil

关键词： Image captioning image understanding Turkish image captioning contrastive language-image pretraining bidirectional encoder representations from transformers image and natural language processing

来源：评论

学校读者我要写书评

暂无评论

Developing an ICD-10 Coding Assistant: Pilot Study Using RoBERTa and GPT-4 for Term Extraction and Description- Based Code Selection

引用

JMIR FORMATIVE RESEARCH 2025年 9卷 e60095页

作者： Puts, Sander Zegers, Catharina M. L. Dekker, Andre Bermejo, Inigo Maastricht Univ GROW Res Inst Oncol & Reprod Med Ctr Dept Radiat Oncol Maastro POB 616 NL-6200 MD Maastricht Netherlands Hasselt Univ Data Sci Inst DSI Hasselt Belgium

Background: The International Classification of Diseases (ICD), developed by the World Health Organization, standardizes health condition coding to support health care policy, research, and billing, but artificial intelligence automation, while promising, still underperforms compared with human accuracy and lacks the explainability needed for adoption in medical settings. Objective: The potential of large language models for assisting medical coders in the ICD-10 coding was explored through the development of a computer-assisted coding system. This study aimed to augment human coding by initially identifying lead terms and using retrieval-augmented generation (RAG)-based methods for computer-assisted coding enhancement. Methods: The explainability dataset from the CodiEsp challenge (CodiEsp-X) was used, featuring 1000 Spanish clinical cases annotated with ICD-10 codes. A new dataset, CodiEsp-X-lead, was generated using GPT-4 to replace full-textual evidence annotations with lead term annotations. A Robustly Optimized BERT (bidirectional encoder representations from transformers) Pretraining Approach transformer model was fine-tuned for named entity recognition to extract lead terms. GPT-4 was subsequently employed to generate code descriptions from the extracted textual evidence. Using a RAG approach, ICD codes were assigned to the lead terms by querying a vector database of ICD code descriptions with OpenAI's text-embedding-ada-002 model. Results: The fine-tuned Robustly Optimized BERT Pretraining Approach achieved an overall F1-score of 0.80 for ICD lead term extraction on the new CodiEsp-X-lead dataset. GPT-4-generated code descriptions reduced retrieval failures in the RAG approach by approximately 5% for both diagnoses and procedures. However, the overall explainability F1-score for the CodiEsp-X task was limited to 0.305, significantly lower than the state-of-the-art F1-score of 0.633. The diminished performance was partly due to the reliance on code descripti

关键词： International Classification of Diseases ICD-10 computer-assisted-coding GPT-4 coding term extraction code analysis computer assisted coding transformer model artificial intelligence AI automation retrieval-augmented generation RAG large language model LLM bidirectional encoder representations from transformers Robustly Optimized BERT Pretraining Approach RoBERTa named entity recognition NER

来源：评论

学校读者我要写书评

暂无评论

A comprehensive strategy for identifying plagiarism in academic submissions

引用

Journal of Umm Al-Qura University for Engineering and Architecture 2025年第2期16卷 310-325页

作者： Setu, Deblina Mazumder Islam, Tania Erfan, Md. Dey, Samrat Kumar Asif, Md. Rashid Al Samsuddoha, Md. Department of Computer Science and Engineering University of Barishal Barishal 8200 Bangladesh School of Science and Technology (SST) Bangladesh Open University (BOU) Gazipur 1705 Bangladesh

Plagiarism is a major problem in education, especially in higher education environments. To address this problem, a comprehensive detection method is proposed, utilizing cutting-edge models like bidirectional encoder representations from transformers (BERT) and cosine similarity for detecting direct copy and semantic changes. The methodology involves evaluating the BERT model and cosine similarity first, then performing pairwise comparisons to determine how similar a student’s work is to a reference text. If the semantic changes are higher, the content is classified as paraphrased;otherwise, it is considered direct copy in cases where the similarity threshold is greater than 30%. When the similarity threshold is less than or equal to 30%, the method moves to online plagiarism detection. This includes web scraping, generating search queries, and employing Term Frequency-Inverse Document Frequency (TF-IDF) vectorization with cosine similarity to assess the similarities between students’ content and online content. As a result, the study detects online plagiarism and distinguishes between plagiarizing and authentic content. The purpose is to provide an effective method to maintain academic integrity and enhance originality in student work. In similarity testing on MIT plagiarism detection datasets, this study demonstrated an average recall of 80%, precision of 68%, accuracy of 71%, and f1-score of 74%. With a threshold of 0.2, the average precision for online plagiarism detection reached 65%, accompanied by a 56% recall, an accuracy of 60%, and an f1-score of 60%. The results of the study demonstrate the efficiency of the strategy in enhancing plagiarism detection accuracy while reducing false positives. © The Author(s) 2025.

关键词： bidirectional encoder representations from transformers Cosine similarity Paraphrasing Plagiarism Term frequency-inverse document frequency Web scraping

来源：评论

学校读者我要写书评

暂无评论

Contextual Embeddings in Sociological Research: Expanding the Analysis of Sentiment and Social Dynamics

引用

SOCIOLOGICAL METHODOLOGY 2025年第1期55卷 25-58页

作者： Mostafavi, Moeen Porter, Michael D. Robinson, Dawn T. Univ Virginia Dept Syst & Informat Engn 351 McCormick Rd Thornton Hall Room D221 Charlottesville VA 22901 USA Univ Virginia Syst & Informat Engn & Data Sci Charlottesville VA USA Univ Georgia Dept Sociol Athens GA USA

The authors introduce BERTNN (bidirectional encoder representations from transformers Neural Network), a novel methodology designed to expand affective lexicons, a critical component in sociological research. BERTNN estimates the affective meanings and their distribution for new concepts, bypassing the need for extensive surveys by leveraging their contextual usage in language. The cornerstone of BERTNN is the use of nuanced word embeddings from bidirectional encoder representations from transformers. BERTNN uniquely encodes words within the framework of synthesized social event sentences, preserving their meaning across actor-behavior-object positions. The model is fine-tuned on the basis of the implied sentiment changes, providing a more refined estimation of affective meanings. BERTNN outperforms previous approaches, setting a new standard in deriving multidimensional affective meanings for novel concepts. It efficiently replicates sentiment ratings that traditionally require extensive survey hours, demonstrating the power of automated modeling in sociological research. The expanded affective lexicons that can be produced with BERTNN cater to shifting cultural meanings and diverse subgroups, demonstrating the potential of computational linguistics to enrich the measurement tools in sociological research. This article underscores the novelty and significance of BERTNN in the broader context of sociological methodology.

关键词： affect control theory sentiment mining sentence embedding computational social science affective lexicon bidirectional encoder representations from transformers BERT contextual word embeddings

来源：评论

学校读者我要写书评

暂无评论

A Hybrid Model Integrating LDA, BERT, and Clustering for Enhanced Topic Modeling

引用

Quality and Quantity 2025年 1-28页

作者： Yadav, Arun Kumar Gupta, Tushar Kumar, Mohit Yadav, Divakar Department of CSE NIT Hamirpur (HP) Hamirpur India SOCIS IGNOU New Delhi India

Topic modeling is a popular machine learning technique in natural language processing for identifying themes within unstructured text. One of the most prominent methods for this purpose is Latent Dirichlet Allocation (LDA), which can automatically uncover topics from large text corpora. However, LDA alone may not always provide the best results. Using bidirectional encoder representations from transformers (BERT) embeddings in topic modeling, significantly enhances the quality and coherence of discovered topics by leveraging deep contextual representations of words. Clustering is another powerful unsupervised machine learning technique frequently used for topic modeling and information extraction from unstructured text. This study introduces a hybrid approach that combines LDA with BERT for enhanced topic modeling, incorporating dimensionality reduction-based clustering. To manage the increasing complexity and computational load of clustering with many features, Uniform Manifold Approximation and Projection is utilized for dimensionality reduction. Experiments conducted on benchmark datasets, specifically Reuters-21578 and 20newsgroups, illustrate the effectiveness of this cluster-informed topic modeling framework. The empirical results suggest that integrating clustering with BERT-LDA for topic modeling can be highly effective, as dimensionality reduction via clustering helps derive more cohesive topics. The study evaluates coherence scores using the BERT-LDA model on the 20newsgroups and Reuters datasets. For the 20newsgroups dataset, BERT-LDA shows a significant improvement in coherence scores: nearly 59% for 10 topics, 42% for 20 topics, 11% for 50 topics, and 16% for 98 topics. Similarly, for the Reuters dataset, coherence scores improved by about 85% for 10 topics, 63% for 20 topics, 43% for 50 topics, and 41% for 98 topics. These results highlight how BERT-LDA enhances topic coherence compared to traditional models. © The Author(s), under exclusive licence to

关键词： bidirectional encoder representations from transformers Dimensionality reduction k-means clustering Latent dirichlet allocation

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：