Proceedings of the Conference Recent Advances in Natural Language Processing - Deep Learning for Natural Language Processing Me 2021
DOI: 10.26615/978-954-452-072-4_058
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Personality Predictive Lexical Cues and Their Correlations

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“…Supervised learning has been the predominant approach, relying on text datasets labelled with personality traits via either self-report or crowdsourced annotations. Among many others, log-linear models (Volkova et al, 2015), random forests , GloVe embeddings with Gaussian processes (Arnoux et al, 2017), recurrent neural networks (Liu et al, 2017), convolutional neural networks (Majumder et al, 2017), support vector machines (Lan and Paraboni, 2018), ridge regression (He and de Melo, 2021), graphical networks (Yang et al, 2021b) and transformers (Kreuter et al, 2022) have been used. Another popular, psycho-linguistically motivated approach are dictionaries/lexicons (e.g., Oberlander and Nowson, 2006;Sinha et al, 2015;Das and Das, 2017).…”
Section: Current Tpc In a Nutshellmentioning
confidence: 99%
“…Supervised learning has been the predominant approach, relying on text datasets labelled with personality traits via either self-report or crowdsourced annotations. Among many others, log-linear models (Volkova et al, 2015), random forests , GloVe embeddings with Gaussian processes (Arnoux et al, 2017), recurrent neural networks (Liu et al, 2017), convolutional neural networks (Majumder et al, 2017), support vector machines (Lan and Paraboni, 2018), ridge regression (He and de Melo, 2021), graphical networks (Yang et al, 2021b) and transformers (Kreuter et al, 2022) have been used. Another popular, psycho-linguistically motivated approach are dictionaries/lexicons (e.g., Oberlander and Nowson, 2006;Sinha et al, 2015;Das and Das, 2017).…”
Section: Current Tpc In a Nutshellmentioning
confidence: 99%