Results 61 to 70 of about 4,714,839 (322)

From Lab to Landscape: Environmental Biohybrid Robotics for Ecological Futures

open access: yesAdvanced Robotics Research, EarlyView.
This Perspective explores environmental biohybrid robotics, integrating living tissues, microorganisms, and insects for operation in real‐world ecosystems. It traces the leap from laboratory experiments to forests, wetlands, and urban environments and discusses key challenges, development pathways, and opportunities for ecological monitoring and ...
Miriam Filippi
wiley   +1 more source

Sarcasm Detection Task.

open access: yes, 2015
Sarcasm Detection Task.
Hichem Slama (298991)   +6 more
core   +1 more source

On the Prospects for African Philosophy in Australia

open access: yesAustralian Journal of Social Issues, EarlyView.
ABSTRACT This paper grapples with the situation of people of African descent in Australia by working through the constitution of the body of academic philosophy in the country. It contends with the parochialism of the Australian philosophical community and the prospects for the cultivation of greater pluralism. Taking African philosophy as one possible
Bryan Mukandi
wiley   +1 more source

Harnessing Context Incongruity for Sarcasm Detection [PDF]

open access: yesProceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 2: Short Papers), 2015
The relationship between context incongruity and sarcasm has been studied in linguistics. We present a computational system that harnesses context incongruity as a basis for sarcasm detection. Our statistical sarcasm classifiers incorporate two kinds of incongruity features: explicit and implicit. We show the benefit of our incongruity features for two
Aditya Joshi 0001   +2 more
openaire   +1 more source

Sarcasm Detection in Tweets using Machine Learning

open access: yes, 2019
<p>This is a machine learning project that aims at classifying the tweets as "sarcastic" or "non-sarcastic" using traditional machine learning methods.</p> <p>If you are interested in knowing more about the project,
Darshan Kasat
core   +1 more source

The Politics of Framing the Student Problem: Inquiries Into Australian Civics Education, 2006–2024

open access: yesAustralian Journal of Social Issues, EarlyView.
ABSTRACT Recurring debates about civics, the kinds of history that should, and should not, be taught in school, and ‘standards debates’ about the ‘basics’ typically follow on the heels of recurring moral panics about the ‘declining’ state of ‘our’ education system.
Patrick O'Keeffe   +2 more
wiley   +1 more source

Detecting Sarcasm in Multimodal Social Platforms [PDF]

open access: yesProceedings of the 24th ACM international conference on Multimedia, 2016
10 pages, 3 figures, final version published in the Proceedings of ACM Multimedia ...
Rossano Schifanella   +3 more
openaire   +2 more sources

‘Let's talk about the weather’: The activist curriculum and global climate change education

open access: yesBritish Educational Research Journal, EarlyView.
Abstract Activist movements have garnered significant global attention on a range of sustainability issues, often involving collectives of citizens coming together. Invoked is the idea of citizens informed to act, emerging not from a common‐sense understanding of everyday life, but rather from a deep political understanding of the world—one that is ...
Richard Pountney
wiley   +1 more source

Artificial Intelligence-based Natural Language Processing for sarcasm detection and classification on Arabic Corpus

open access: yesAlexandria Engineering Journal
Sarcasm is a type of communication designed to harass or mock an individual using words against their accurate meaning. It signifies a negative sentiment but a positive sentiment.
Wala bin Subait   +7 more
doaj   +1 more source

Sarcasm Relation to Time: Sarcasm Detection with Temporal Features and Deep Learning

open access: yes, 2022
Abstract This paper discusses a framework used to detect sarcasm in relation to time. It uses a set of deep learning extracted features (deep features) combined with a set of handcrafted features. The results of the experiments are positive in terms of Accuracy, Precision, Recall and F1-measure. The combination of features is classified using a
Md Saifullah Razali   +4 more
openaire   +2 more sources

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