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A review of the use of portable technologies as observational aids in the classroom
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Joint modeling of users, questions and answers for answer selection in CQA
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Changes in science attitudes, beliefs, knowledge and physiological arousal after implementation of a multimodal, cooperative intervention in primary school science classes
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Characterising postgraduate students’ corpus query and usage patterns for disciplinary data-driven learning
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Learning factorized representations for open-set domain adaptation
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Multilevel topic dependency models for assessment design and delivery: A hypergraph based approach
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Detecting and visualizing context and stress via a fuzzy rule-based system during commuter driving
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Discovering correlations between sparse features in distant supervision for relation extraction
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Engagement and performance in a first year natural resource science course
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Deep context of citations using machine-learning models in scholarly full-text articles
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Abstract:
Information retrieval systems for scholarly literature rely heavily not only on text matching but on semantic- and context-based features. Readers nowadays are deeply interested in how important an article is, its purpose and how influential it is in follow-up research work. Numerous techniques to tap the power of machine learning and artificial intelligence have been developed to enhance retrieval of the most influential scientific literature. In this paper, we compare and improve on four existing state-of-the-art techniques designed to identify influential citations. We consider 450 citations from the Association for Computational Linguistics corpus, classified by experts as either important or unimportant, and further extract 64 features based on the methodology of four state-of-the-art techniques. We apply the Extra-Trees classifier to select 29 best features and apply the Random Forest and Support Vector Machine classifiers to all selected techniques. Using the Random Forest classifier, our supervised model improves on the state-of-the-art method by 11.25%, with 89% Precision-Recall area under the curve. Finally, we present our deep-learning model, the Long Short-Term Memory network, that uses all 64 features to distinguish important and unimportant citations with 92.57% accuracy.
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Keyword:
1706 Computer Science Applications; 3300 Social Sciences; 3309 Library and Information Sciences; Classification; Quality; Rather
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URL: https://espace.library.uq.edu.au/view/UQ:d1f3d86
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Using an online social media space to engage parents in student learning in the early-years: enablers and impediments
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Willis, Linda-Dianne; Exley, Beryl. - : Universitat de Barcelona * Grup de Recerca Ensenyament i Aprenentatge Virtual, Observatorio de la Educacion Digital (OED), 2018
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Measuring communication difficulty through effortful speech production during conversation
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An investigation of Chinese postgraduate students' experiences on a data-visualized English writing feedback platform
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Dai, Kun. - : Institute of Electrical and Electronics Engineers, 2017
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Social moments: A perspective on interaction for social robotics
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Retesting the limits of data-driven learning: feedback and error correction
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Searching for My Lady’s Bonnet: discovering poetry in the National Library of Australia’s newspapers database
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An investigation of differences and changes in L2 writing anxiety between blended and conventional english language learning context
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