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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 8 of 8 1 Measurement of $W^{\pm}$-boson and $Z$-boson production cross-sections in $pp$ collisions at $\sqrt{s}=2.76$ TeV with the ATLAS detector Aad, Georges; Abbott, Brad; Abbott, Dale Charles. - 2019 BASE Show details 2 A Feasible Framework for Arbitrary-Shaped Scene Text Recognition ... Zhang, Jinjin; Wang, Wei; Huang, Di. - : arXiv, 2019 BASE Show details 3 Additional file 1: of Molluscicidal effectiveness of Luo-Wei, a novel plant-derived molluscicide, against Oncomelania hupensis, Biomphalaria alexandrina and Bulinus truncatus ... Tie-Wu Jia; Wang, Wei; Sun, Le-Ping. - : figshare, 2019 BASE Show details 4 Additional file 1: of Molluscicidal effectiveness of Luo-Wei, a novel plant-derived molluscicide, against Oncomelania hupensis, Biomphalaria alexandrina and Bulinus truncatus ... Tie-Wu Jia; Wang, Wei; Sun, Le-Ping. - : figshare, 2019 BASE Show details 5 Antonym-Synonym Classification Based on New Sub-space Embeddings ... Ali, Muhammad Asif; Sun, Yifang; Zhou, Xiaoling; Wang, Wei; Zhao, Xiang. - : arXiv, 2019 Abstract: Distinguishing antonyms from synonyms is a key challenge for many NLP applications focused on the lexical-semantic relation extraction. Existing solutions relying on large-scale corpora yield low performance because of huge contextual overlap of antonym and synonym pairs. We propose a novel approach entirely based on pre-trained embeddings. We hypothesize that the pre-trained embeddings comprehend a blend of lexical-semantic information and we may distill the task-specific information using Distiller, a model proposed in this paper. Later, a classifier is trained based on features constructed from the distilled sub-spaces along with some word level features to distinguish antonyms from synonyms. Experimental results show that the proposed model outperforms existing research on antonym synonym distinction in both speed and performance. ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1906.05612 https://dx.doi.org/10.48550/arxiv.1906.05612 BASE Hide details 6 Prelicensure nursing students' perspectives on video-assisted debriefing following high fidelity simulation: A qualitative study Zhang, H.; Goh, S. H. L.; Wu, X. V.... In: Research outputs 2014 to 2021 (2019) BASE Show details 7 The relationship between procrastination, perceived stress, saliva alpha-amylase level and parenting styles in Chinese first year medical students Khalid, Arslan; Zhang, Qian; Wang, Wei. - : Dove, 2019 BASE Show details 8 Joint Multi-Label Attention Networks for Social Text Annotation Huang, Kaizhu; Coenen, FP; Dong, Hang. - 2019 BASE Show details

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