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Grad-SAM: Explaining Transformers via Gradient Self-Attention Maps ...
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Universal Dependencies and Semantics for English and Hebrew Child-directed Speech
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In: Proceedings of the Society for Computation in Linguistics (2022)
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Forecasting the macrolevel determinants of entrepreneurial opportunities using artificial intelligence models
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In: ISSN: 0040-1625 ; Technological Forecasting and Social Change ; https://hal.archives-ouvertes.fr/hal-03442122 ; Technological Forecasting and Social Change, Elsevier, 2021, pp.121353. ⟨10.1016/j.techfore.2021.121353⟩ (2021)
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Abstract:
International audience ; To date, entrepreneurship researchers have tended to avoid state-of-the-art artificial intelligence techniques; in this paper, we fill that gap. Based on eclectic entrepreneurship theory, we present an original work that uses artificial intelligence to forecast the macrolevel determinants of entrepreneurial opportunity. Modern artificial intelligence could open new areas for future research opportunities in entrepreneurship and help close the gap between theory and practice. Our empirical analysis offers two major results by using a panel dataset of 149 countries covering 2007–2018 and six machine-learning models. First, entrepreneurs prefer to exploit opportunities in countries with stable economic governance that provide high education standards, health, social capital, and a safe, natural environment. Second, CatBoost regression performs better in predicting entrepreneurial opportunity compared to linear regression and more advanced machine-learning models. Recommendations for policy-makers and managers and directions for future studies are also discussed.
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Keyword:
[SHS.ECO]Humanities and Social Sciences/Economics and Finance; [SHS.GESTION]Humanities and Social Sciences/Business administration; Artificial intelligence; Eclectic theory of entrepreneurship; Entrepreneurial opportunity
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URL: https://doi.org/10.1016/j.techfore.2021.121353 https://hal.archives-ouvertes.fr/hal-03442122
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On Neurons Invariant to Sentence Structural Changes in Neural Machine Translation ...
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A constraint on co-occurrence of partitive quantifiers and gradable predicates
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In: Sinn und Bedeutung; Bd. 25 (2021): Proceedings of Sinn und Bedeutung 25; 55-72 ; Proceedings of Sinn und Bedeutung; Vol 25 (2021): Proceedings of Sinn und Bedeutung 25; 55-72 ; 2629-6055 (2021)
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Cross-linguistically Consistent Semantic and Syntactic Annotation of Child-directed Speech ...
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On the Relation between Syntactic Divergence and Zero-Shot Performance ...
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The Grammar-Learning Trajectories of Neural Language Models ...
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A diachronic explanation for cross-linguistic variation in the use of inverse-scope constructions
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In: Semantics and Linguistic Theory; Proceedings of SALT 31; 021-041 ; 2163-5951 (2021)
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From Unsupervised Machine Translation To Adversarial Text Generation ...
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Language (Re)modelling: Towards Embodied Language Understanding ...
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Semantic Structural Decomposition for Neural Machine Translation ...
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Comparison by Conversion: Reverse-Engineering UCCA from Syntax and Lexical Semantics ...
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Non-maximality and homogeneity: Parallels between collective predicates and absolute adjectives
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In: Sinn und Bedeutung; Bd. 24 Nr. 1 (2020): Proceedings of Sinn und Bedeutung 24; 66-83 ; Proceedings of Sinn und Bedeutung; Vol 24 No 1 (2020): Proceedings of Sinn und Bedeutung 24; 66-83 ; 2629-6055 (2020)
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