Academic literature on the topic 'Automatic Stance Detection'
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Journal articles on the topic "Automatic Stance Detection"
Yan, Yilin, Jonathan Chen, and Mei-Ling Shyu. "Efficient Large-Scale Stance Detection in Tweets." International Journal of Multimedia Data Engineering and Management 9, no. 3 (July 2018): 1–16. http://dx.doi.org/10.4018/ijmdem.2018070101.
Full textGhimire, Niroj, and Surendra Shrestha. "Fake News Stance Detection using Deep Neural Network." Journal of Lumbini Engineering College 4, no. 1 (December 7, 2022): 49–53. http://dx.doi.org/10.3126/lecj.v4i1.49366.
Full textWillemsen, A. T. M., F. Bloemhof, and H. B. K. Boom. "Automatic stance-swing phase detection from accelerometer data for peroneal nerve stimulation." IEEE Transactions on Biomedical Engineering 37, no. 12 (1990): 1201–8. http://dx.doi.org/10.1109/10.64463.
Full textMartínez, Rubén Yáñez, Guillermo Blanco, and Anália Lourenço. "Spanish Corpora of tweets about COVID-19 vaccination for automatic stance detection." Information Processing & Management 60, no. 3 (May 2023): 103294. http://dx.doi.org/10.1016/j.ipm.2023.103294.
Full textStede, Manfred. "Automatic argumentation mining and the role of stance and sentiment." Journal of Argumentation in Context 9, no. 1 (May 4, 2020): 19–41. http://dx.doi.org/10.1075/jaic.00006.ste.
Full textLidstone, Daniel E., Louise M. Porcher, Jessica DeBerardinis, Janet S. Dufek, and Mohamed B. Trabia. "Concurrent Validity of an Automated Footprint Detection Algorithm to Measure Plantar Contact Area During Walking." Journal of the American Podiatric Medical Association 109, no. 6 (November 1, 2019): 416–25. http://dx.doi.org/10.7547/17-118.
Full textHouliston, B. R., A. F. Merry, and D. T. Parry. "TADAA: Towards Automated Detection of Anaesthetic Activity." Methods of Information in Medicine 50, no. 05 (2011): 464–71. http://dx.doi.org/10.3414/me11-02-0001.
Full textOmero, Paolo, Massimiliano Valotto, Riccardo Bellana, Ramona Bongelli, Ilaria Riccioni, Andrzej Zuczkowski, and Carlo Tasso. "Writer’s uncertainty identification in scientific biomedical articles: a tool for automatic if-clause tagging." Language Resources and Evaluation 54, no. 4 (June 11, 2020): 1161–81. http://dx.doi.org/10.1007/s10579-020-09491-8.
Full textKarande, Hema, Rahee Walambe, Victor Benjamin, Ketan Kotecha, and TS Raghu. "Stance detection with BERT embeddings for credibility analysis of information on social media." PeerJ Computer Science 7 (April 14, 2021): e467. http://dx.doi.org/10.7717/peerj-cs.467.
Full textBriggs, Eloise V., and Claudia Mazzà. "Automatic methods of hoof-on and -off detection in horses using wearable inertial sensors during walk and trot on asphalt, sand and grass." PLOS ONE 16, no. 7 (July 26, 2021): e0254813. http://dx.doi.org/10.1371/journal.pone.0254813.
Full textDissertations / Theses on the topic "Automatic Stance Detection"
Dias, Marcelo dos Santos. "Detecção não supervisionada de posicionamento em textos de tweets." reponame:Biblioteca Digital de Teses e Dissertações da UFRGS, 2017. http://hdl.handle.net/10183/169098.
Full textStance Detection is the task of automatically identifying if the author of a text is in favor of the given target, against the given target, or whether neither inference is likely. With the wide use of Twitter as a platform to express opinions and stances, the automatic analysis of this content becomes of high regard for companies, organizations and public figures. In general, works that explore such task adopt supervised or semi-supervised approaches. The present work proposes and evaluates a non-supervised process to detect stance in texts of tweets that has as entry only the target and a set of tweets to classify and is based on a hybrid approach composed by 2 stages: a) automatic labelling of tweets based on a set of heuristics and b) complementary classification based on supervised machine learning. The proposal succeeds when applied to public figures, overcoming the state-of-the-art. Beyond that, some alternatives are evaluated with the intention of increasing the performance when applied to other domains, revealing the possibility of use of strategies such as using seed targets and profiles depending on each domain characteristics.
Kanhere, Neeraj Krantiveer. "Vision-based detection, tracking and classification of vehicles using stable features with automatic camera calibration." Connect to this title online, 2008. http://etd.lib.clemson.edu/documents/1219861574/.
Full textcignarella, alessandra teresa. "Dependency Syntax in the Automatic Detection of Irony and Stance." Doctoral thesis, 2021. http://hdl.handle.net/2318/1873580.
Full textRenda, Alessandro. "Algorithms and techniques for data stream mining." Doctoral thesis, 2021. http://hdl.handle.net/2158/1235915.
Full textBook chapters on the topic "Automatic Stance Detection"
Zhang, Yanjing, Jianming Cui, and Ming Liu. "Research on Adversarial Patch Attack Defense Method for Traffic Sign Detection." In Communications in Computer and Information Science, 199–210. Singapore: Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-8285-9_15.
Full textWang, Yan. "Fast Detection and Automatic Parameter Estimation of a Gravitational Wave Signal with a Novel Method." In First-stage LISA Data Processing and Gravitational Wave Data Analysis, 205–15. Cham: Springer International Publishing, 2015. http://dx.doi.org/10.1007/978-3-319-26389-2_12.
Full textYan, Yilin, Jonathan Chen, and Mei-Ling Shyu. "Efficient Large-Scale Stance Detection in Tweets." In Deep Learning and Neural Networks, 667–83. IGI Global, 2020. http://dx.doi.org/10.4018/978-1-7998-0414-7.ch037.
Full textBarker, Zoe E., Nick J. Bell, Jonathan R. Amory, and Edward A. Codling. "Developments in automated systems for monitoring livestock health: lameness." In Advances in precision livestock farming, 247–88. Burleigh Dodds Science Publishing, 2022. http://dx.doi.org/10.19103/as.2021.0090.10.
Full textZou, Dehua, Zhipeng Jiang, Minmin Qiao, Lanlan Liu, Wei Jiang, and Qianwei Yi. "Analysis and Simulation of Dynamic Characteristics for Multi-Split Transmission Line Splicing Pipe Flaw Detection Robot." In Advances in Transdisciplinary Engineering. IOS Press, 2022. http://dx.doi.org/10.3233/atde220494.
Full text"Intrusion Detection Systems for (Wireless) Automation Systems." In The State of the Art in Intrusion Prevention and Detection, 449–66. Auerbach Publications, 2014. http://dx.doi.org/10.1201/b16390-23.
Full textTang, Yongmei, Xiangyun Liao, Weixin Si, and Zhigang Ning. "Prediction of Alzheimer’s Disease Based on Coordinate-Dense Attention Network." In Proceedings of CECNet 2021. IOS Press, 2021. http://dx.doi.org/10.3233/faia210390.
Full textSklar, Larry A. "The Future of Flow Cytometry in Biotechnology: The Response to Diversity and Complexity." In Flow Cytometry for Biotechnology. Oxford University Press, 2005. http://dx.doi.org/10.1093/oso/9780195183146.003.0004.
Full textConference papers on the topic "Automatic Stance Detection"
Gupta, Anuradha, and Shikha Mehta. "Automatic Stance Detection for Twitter Data." In 2022 1st International Conference on Informatics (ICI). IEEE, 2022. http://dx.doi.org/10.1109/ici53355.2022.9786920.
Full textMohtarami, Mitra, Ramy Baly, James Glass, Preslav Nakov, Lluís Màrquez, and Alessandro Moschitti. "Automatic Stance Detection Using End-to-End Memory Networks." In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/n18-1070.
Full textConforti, Costanza, Mohammad Taher Pilehvar, and Nigel Collier. "Towards Automatic Fake News Detection: Cross-Level Stance Detection in News Articles." In Proceedings of the First Workshop on Fact Extraction and VERification (FEVER). Stroudsburg, PA, USA: Association for Computational Linguistics, 2018. http://dx.doi.org/10.18653/v1/w18-5507.
Full textChristhie, William, Julio C. S. Reis, Fabrício Benevenuto Mirella M. Moro, and Virgílio Almeida. "Detecção de Posicionamento em Tweets sobre Política no Contexto Brasileiro." In VII Brazilian Workshop on Social Network Analysis and Mining. Sociedade Brasileira de Computação - SBC, 2018. http://dx.doi.org/10.5753/brasnam.2018.3583.
Full textSantos, Patricia D., and Denise H. Goya. "Automatic Twitter Stance Detection on Politically Controversial Issues: A Study on Covid-19’s CPI." In Encontro Nacional de Inteligência Artificial e Computacional. Sociedade Brasileira de Computação - SBC, 2021. http://dx.doi.org/10.5753/eniac.2021.18281.
Full textKotelnikov, Evgeny, Natalia Loukachevitch, Irina Nikishina, and Alexander Panchenko. "RuArg-2022: Argument Mining Evaluation." In Dialogue. RSUH, 2022. http://dx.doi.org/10.28995/2075-7182-2022-21-333-348.
Full textDow, Blaine, Pierrick Ferrando, N. I. Abolins, Tom Leonard, Ahmed Abuelaish, Nicolas Gallinal Cuenca, Jerry Hansen, and Freddy Rojas Rodriguez. "Advancing Influx Detection Toward Automated Well Control." In IADC/SPE International Drilling Conference and Exhibition. SPE, 2022. http://dx.doi.org/10.2118/208750-ms.
Full textKrikor, Ara, Shreepad Purushottam Khambete, Paulinus Abhyudaya Bimastianto, Michael Bradley Cotten, Lucian Toader, Fernando Jose Landaeta Rivas, Shahid Yakubbhai Duivala, et al. "Machine Learning Delivers Automated Feedback on Real Time Key Performance Indicators During Drilling Operations." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211753-ms.
Full textAl Radi, Muaz, Hamad Karki, Naoufel Werghi, Sajid Javed, and Jorge Dias. "Video Analysis of Flare Stacks with an Autonomous Low-Cost Aerial System." In ADIPEC. SPE, 2022. http://dx.doi.org/10.2118/211007-ms.
Full textKotonya, Neema, and Francesca Toni. "Gradual Argumentation Evaluation for Stance Aggregation in Automated Fake News Detection." In Proceedings of the 6th Workshop on Argument Mining. Stroudsburg, PA, USA: Association for Computational Linguistics, 2019. http://dx.doi.org/10.18653/v1/w19-4518.
Full textReports on the topic "Automatic Stance Detection"
Berney, Ernest, Andrew Ward, and Naveen Ganesh. First generation automated assessment of airfield damage using LiDAR point clouds. Engineer Research and Development Center (U.S.), March 2021. http://dx.doi.org/10.21079/11681/40042.
Full textYan, Yujie, and Jerome F. Hajjar. Automated Damage Assessment and Structural Modeling of Bridges with Visual Sensing Technology. Northeastern University, May 2021. http://dx.doi.org/10.17760/d20410114.
Full textFang, Chen. Unsettled Issues in Vehicle Autonomy, Artificial Intelligence, and Human-Machine Interaction. SAE International, April 2021. http://dx.doi.org/10.4271/epr2021010.
Full textSeginer, Ido, Louis D. Albright, and Robert W. Langhans. On-line Fault Detection and Diagnosis for Greenhouse Environmental Control. United States Department of Agriculture, February 2001. http://dx.doi.org/10.32747/2001.7575271.bard.
Full textGalili, Naftali, Roger P. Rohrbach, Itzhak Shmulevich, Yoram Fuchs, and Giora Zauberman. Non-Destructive Quality Sensing of High-Value Agricultural Commodities Through Response Analysis. United States Department of Agriculture, October 1994. http://dx.doi.org/10.32747/1994.7570549.bard.
Full textEngel, Bernard, Yael Edan, James Simon, Hanoch Pasternak, and Shimon Edelman. Neural Networks for Quality Sorting of Agricultural Produce. United States Department of Agriculture, July 1996. http://dx.doi.org/10.32747/1996.7613033.bard.
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