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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 – 14 of 14 1 Finetuning Pretrained Transformers into RNNs ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Chen, Weizhu; Ilharco, Gabriel. - : Underline Science Inc., 2021 BASE Show details 2 A Call for More Rigor in Unsupervised Cross-lingual Learning ... Artetxe, Mikel; Ruder, Sebastian; Yogatama, Dani. - : arXiv, 2020 BASE Show details 3 Learning and Evaluating General Linguistic Intelligence ... Yogatama, Dani; d'Autume, Cyprien de Masson; Connor, Jerome. - : arXiv, 2019 BASE Show details 4 On the Cross-lingual Transferability of Monolingual Representations ... Artetxe, Mikel; Ruder, Sebastian; Yogatama, Dani. - : arXiv, 2019 BASE Show details 5 Learning to Compose Words into Sentences with Reinforcement Learning ... Yogatama, Dani; Blunsom, Phil; Dyer, Chris. - : arXiv, 2016 BASE Show details 6 Sparse Overcomplete Word Vector Representations ... Faruqui, Manaal; Tsvetkov, Yulia; Yogatama, Dani. - : arXiv, 2015 BASE Show details 7 Learning Word Representations with Hierarchical Sparse Coding ... Yogatama, Dani; Manaal Faruqui; Dyer, Chris. - : Carnegie Mellon University, 2015 BASE Show details 8 Learning Word Representations with Hierarchical Sparse Coding ... Yogatama, Dani; Manaal Faruqui; Dyer, Chris. - : Carnegie Mellon University, 2015 BASE Show details 9 Linguistic Structured Sparsity in Text Categorization ... Yogatama, Dani; Smith, Noah A.. - : Carnegie Mellon University, 2014 Abstract: We introduce three linguistically motivated structured regularizers based on parse trees, topics, and hierarchical word clusters for text categorization. These regularizers impose linguistic bias in feature weights, enabling us to incorporate prior knowledge into conventional bagof-words models. We show that our structured regularizers consistently improve classification accuracies compared to standard regularizers that penalize features in isolation (such as lasso, ridge, and elastic net regularizers) on a range of datasets for various text prediction problems: topic classification, sentiment analysis, and forecasting. ... Keyword: 89999 Information and Computing Sciences not elsewhere classified; FOS Computer and information sciences URL: https://kilthub.cmu.edu/articles/Linguistic_Structured_Sparsity_in_Text_Categorization/6473504 https://dx.doi.org/10.1184/r1/6473504 BASE Hide details 10 Linguistic Structured Sparsity in Text Categorization ... Yogatama, Dani; Smith, Noah A.. - : Carnegie Mellon University, 2014 BASE Show details 11 Learning Word Representations with Hierarchical Sparse Coding ... Yogatama, Dani; Faruqui, Manaal; Dyer, Chris. - : arXiv, 2014 BASE Show details 12 Predicting a Scientific Community’s Response to an Article ... Yogatama, Dani; Heliman, Michael; O'Connor, Brendan. - : Carnegie Mellon University, 2011 BASE Show details 13 Predicting a Scientific Community’s Response to an Article ... Yogatama, Dani; Heliman, Michael; O'Connor, Brendan. - : Carnegie Mellon University, 2011 BASE Show details 14 Part-of-Speech Tagging for Twitter: Annotation, Features, and Experiments Gimpel, Kevin; Schneider, Nathan; O'Connor, Brendan... In: DTIC (2010) BASE Show details

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