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Neural User Factor Adaptation for Text Classification: Learning to Generalize Across Author Demographics

Xiaolei Huang, Michael J. Paul
2019 Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*  
In experiments on four English-language social media datasets, we find that classification performance improves when adapting for user factors.  ...  We propose a multitask neural model to account for demographic variations via adversarial training.  ...  Acknowledgements The authors thank the anonymous reviews for their insightful comments and suggestions.  ... 
doi:10.18653/v1/s19-1015 dblp:conf/starsem/HuangP19 fatcat:pl2vtoywsnbzzgyrrmsibcxrfq

Tackling Racial Bias in Automated Online Hate Detection: Towards Fair and Accurate Classification of Hateful Online Users Using Geometric Deep Learning [article]

Zo Ahmed, Bertie Vidgen, Scott A. Hale
2021 arXiv   pre-print
Geometric deep learning dynamically learns information-rich network representations and can generalise to unseen nodes.  ...  To combat it, technology companies are increasingly identifying and sanctioning 'hateful users' rather than simply moderating hateful content.  ...  The learnt parameters of these functions, shared across nodes in a form analogous to vanilla convolutional neural networks, are then used to generate embeddings for previously unseen nodes.  ... 
arXiv:2103.11806v1 fatcat:et35xmiotzgjbfvdvd44yvzkai

Identifying machine learning techniques for classification of target advertising

Jin-A Choi, Kiho Lim
2020 ICT Express  
Twenty-three machine learning-based online targeted advertising strategies are identified and classified largely into two categories, user-centric and content-centric approaches.  ...  This study investigates and classifies various machine learning techniques that are used to enhance targeted online advertising.  ...  [12] proposed a scheme to learn subdocument classification for contextual advertisement applications when only page level labels are available.  ... 
doi:10.1016/j.icte.2020.04.012 fatcat:5qnbssw625chhfeeqkwzkgcjxm

A Convolution-LSTM-Based Deep Neural Network for Cross-Domain MOOC Forum Post Classification

Xiaocong Wei, Hongfei Lin, Liang Yang, Yuhai Yu
2017 Information  
In this paper, considering the biases among different courses, we propose a transfer learning framework based on a convolutional neural network and a long short-term memory model, called ConvL, to automatically  ...  First, we learn the feature representation for each word by considering the local contextual feature via the convolution operation.  ...  We propose a deep learning framework based on convolution and LSTM that can capture the generic factors of variation present in all of the factors suitable for our three cross-domain MOOC forum post classification  ... 
doi:10.3390/info8030092 fatcat:4mc53e4t4bf4xg4s6jjvru5apu

Neural Contrastive Clustering: Fully Unsupervised Bias Reduction for Sentiment Classification [article]

Jared Mowery
2022 arXiv   pre-print
Conclusions: Neural contrastive clustering reduces correlation bias in sentiment text classification.  ...  Further research is needed to explore generalizing this technique to other neural network architectures and application domains.  ...  A survey of bias in natural language generation reveals considerable challenges [5] , and language models have specifically been shown to exhibit bias in expressed sentiment when generating text based  ... 
arXiv:2204.10467v1 fatcat:eblsafmg55czrhihy63sk4j27a

Early Identification of Depression Severity Levels on Reddit Using Ordinal Classification

Usman Naseem, Adam G. Dunn, Jinman Kim, Matloob Khushi
2022 Proceedings of the ACM Web Conference 2022  
User-generated text on social media is a promising avenue for public health surveillance and has been actively explored for its feasibility in the early identification of depression.  ...  To date, there has been little effort towards identifying users' depression severity level and disregard the inherent ordinal nature across these fine-grain levels.  ...  ACKNOWLEDGMENTS Authors would like to thank Chris, Shaoming, Kexin, Sayedi, and Yizhou for their assistance in labeling and initial experimental setup.  ... 
doi:10.1145/3485447.3512128 fatcat:mjkhebutbvbzpbx2u7qkw76ixa

User-Generated Content (UGC) Credibility on Social Media Using Sentiment Classification

Esraa Afify, Ahmed Sharaf Eldin, Ayman E. Khedr, Fahad Kamal Alsheref
2019 النشرة المعلوماتیة فی الحاسبات والمعلومات  
As a fact, the User-Generated Content (UGC) on social media platforms suffers from a lack of professional gatekeepers to monitor this content.  ...  This paper adapted some of the existing literature and concluded that many previous approaches have investigated information credibility on Twitter and a limited number of Facebook for proposing a new  ...  After filtering the spammers and the fake users; proposing to perform text content detection and clustering credibility for text content on Facebook. (2) The evaluator for the text content consists of  ... 
doi:10.21608/fcihib.2019.107506 fatcat:w4vazjtyl5h6zdz2kol2vn5hsy

Classification of Fake News by Fine-tuning Deep Bidirectional Transformers based Language Model

Akshay Aggarwal, Aniruddha Chauhan, Deepika Kumar, Mamta Mittal, Sharad Verma
2018 EAI Endorsed Transactions on Scalable Information Systems  
This paper demonstrates how even with minimal text pre-processing, the fine-tuned BERT model is robust enough to perform significantly well on the downstream task of classification of news articles.  ...  Transfer learning on the Bidirectional Encoder Representations from Transformers (BERT) language model has been applied for this task.  ...  on various NLP tasks without explicitly being trained for doing those tasks.Transfer learning is a powerful approach that can adapt well to different tasks.  ... 
doi:10.4108/eai.13-7-2018.163973 fatcat:bycjszl7mjadpdpyavkq6qyxxe

A web-based system for neural network based classification in temporomandibular joint osteoarthritis

Priscille de Dumast, Clément Mirabel, Lucia Cevidanes, Antonio Ruellas, Marilia Yatabe, Marcos Ioshida, Nina Tubau Ribera, Loic Michoud, Liliane Gomes, Chao Huang, Hongtu Zhu, Luciana Muniz (+8 others)
2018 Computerized Medical Imaging and Graphics  
Objective-The purpose of this study is to describe the methodological innovations of a webbased system for storage, integration and computation of biomedical data, using a training imaging dataset to remotely  ...  a web-based system that provides advanced shape statistical analysis and a neural network based classification of temporomandibular joint osteoarthritis.  ...  Acknowledgments The authors acknowledge the financial support received from National Institutes of Health (NIH) (grant numbers R01EB021391, R01DE024450, R21DE025306).  ... 
doi:10.1016/j.compmedimag.2018.04.009 pmid:29753964 pmcid:PMC5987251 fatcat:3fvipf3zwbbyro3wiw6jumf7zq

Particle swarm classification: A survey and positioning

Nabila Nouaouria, Mounir Boukadoum, Robert Proulx
2013 Pattern Recognition  
Subsequently, a positioning PSC for these problems with respect to other classification approaches is made.  ...  The solutions that have been proposed in the literature for each of these issues are described including recent improvements by a novel PSC algorithm developed by the authors.  ...  Acknowledgments This work was possible thanks to the financial support of the Natural Sciences and Engineering Research Council of Canada (NSERC).  ... 
doi:10.1016/j.patcog.2012.12.011 fatcat:nh3qmsvucnhxtbzhwq7aq4i3hy

Deep multitask ensemble classification of emergency medical call incidents combining multimodal data improves emergency medical dispatch [article]

Pablo Ferri, Carlos S&aacuteez, Antonio F&eacutelix-De Castro, Javier Juan-Albarrac&iacuten, Vicent Blanes-Selva, Purificaci&oacuten S&aacutenchez-Cuesta, Juan M Garc&iacutea-G&oacutemez
2020 medRxiv   pre-print
factors and free text dispatcher observations.  ...  Conclusion: To our knowledge, this study presents the development of the first deep learning model undertaking emergency medical call incidents classification.  ...  These data consist of demographics, circumstantial factors, clinical featurescollected throughout the triage tree navigation-and free text dispatcher observations: Demographics data-structured and stationaryinclude  ... 
doi:10.1101/2020.06.26.20123216 fatcat:h5qqoycy5fdhhhlewcgpzfx3d4

Performance Evaluation of Naive Bayes and Back Propagation Neural Network classifiers in gestational Diabetes Mellitus Classification

2020 International Journal of Emerging Trends in Engineering Research  
BPNN classifier is a type of ANN classifier that is developed for classification of GDM disease with accuracy estimation.  ...  The classification assessment methods Confusion Matrix (CM) and Balanced Error Rate(BER) are used to compare both the classification methodologies' efficiency and accuracy.  ...  But previous work indicates that the distorted composition of the set of learning may produce poor results in classifying text. The author offers a different way of coping with the case.  ... 
doi:10.30534/ijeter/2020/868102020 fatcat:lfd42vnqvrf4vkqhjuyg7g556i

Real-time processing of social media with SENTINEL: A syndromic surveillance system incorporating deep learning for health classification

Ovidiu Șerban, Nicholas Thapen, Brendan Maginnis, Chris Hankin, Virginia Foot
2018 Information Processing & Management  
It applies deep learning to the problem of classifying health-related tweets and is able to do so with high accuracy.  ...  The ability to detect disease outbreaks earlier than traditional methods would be highly useful for public health officials.  ...  In recent years Deep Neural Networks (DNNs) have set new benchmarks in text classification (Kim, 2014) due to their ability to learn complex representations from the textual data.  ... 
doi:10.1016/j.ipm.2018.04.011 fatcat:seorwli2ovd2bj5v4eoadkxcpa

R$²$BN: An Adaptive Model for Keystroke-Dynamics-Based Educational Level Classification

Ioannis Tsimperidis, Paul D. Yoo, Kamal Taha, Alexios Mylonas, Vasilis Katos
2018 IEEE Transactions on Cybernetics  
In addition, up to today, their focus was primarily gender and age, which seem to be more appropriate for commercial applications (such as developing commercial software), leaving out from research other  ...  For this reason, we examine ways to reduce the time that is needed to build our model, including the use of a novel data condensation method, and discuss the tradeoff between an accurate and a fast prediction  ...  It also includes demographic data to be used for user classification, such as educational level.  ... 
doi:10.1109/tcyb.2018.2869658 pmid:30281507 fatcat:2vzjqxlcqvcrfdtua424zr2quy

A data mining approach for classification of traffic violations types

Nor Aqilah Othman, Cik Feresa Mohd Foozy, Aida Mustapha, Salama A Mostafa, Shamala Palaniappan, Shafiza Ariffin Kashinath
2021 IJAIN (International Journal of Advances in Intelligent Informatics)  
It is also helpful for authorities to strategize and plan ways to reduce traffic violations among road users by studying the most common traffic violation types in an area, whether a citation, a warning  ...  This paper's results could serve as baseline results for investigations related to the classification of traffic violation types.  ...  It is also helpful for authorities to strategize and plan ways to reduce traffic violations among road users by studying the most common traffic violation types in an area, whether a citation, a warning  ... 
doi:10.26555/ijain.v7i3.708 fatcat:ep6kunx5enbrzfoi4a6wc22eo4
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