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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 Page: 1 2 3 Hits 1 – 20 of 41 1 Neural reality of argument structure constructions ... Li, Bai; Zhu, Zining; Thomas, Guillaume. - : arXiv, 2022 BASE Show details 2 On the Use of Linguistic Features for the Evaluation of Generative Dialogue Systems ... Berlot-Attwell, Ian; Rudzicz, Frank. - : arXiv, 2021 BASE Show details 3 TorontoCL at CMCL 2021 Shared Task: RoBERTa with Multi-Stage Fine-Tuning for Eye-Tracking Prediction ... Li, Bai; Rudzicz, Frank. - : arXiv, 2021 BASE Show details 4 Quantifying the Task-Specific Information in Text-Based Classifications ... Zhu, Zining; Balagopalan, Aparna; Ghassemi, Marzyeh. - : arXiv, 2021 BASE Show details 5 An {E}valuation of {D}isentangled {R}epresentation {L}earning for {T}exts ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Hirst, Graeme; Rudzicz, Frank. - : Underline Science Inc., 2021 BASE Show details 6 How is BERT surprised? Layerwise detection of linguistic anomalies ... Li, Bai; Zhu, Zining; Thomas, Guillaume. - : arXiv, 2021 BASE Show details 7 Comparing Pre-trained and Feature-Based Models for Prediction of Alzheimer's Disease Based on Speech Balagopalan, Aparna; Eyre, Benjamin; Robin, Jessica... In: Front Aging Neurosci (2021) BASE Show details 8 Identification of primary and collateral tracks in stuttered speech Riad, Rachid; Bachoud-Lévi, Anne-Catherine; Rudzicz, Frank... In: LREC 2020 - 12th Conference on Language Resources and Evaluation ; https://hal.archives-ouvertes.fr/hal-02959454 ; LREC 2020 - 12th Conference on Language Resources and Evaluation, May 2020, Marseille, France (2020) BASE Show details 9 Semantic coordinates analysis reveals language changes in the AI field ... Zhu, Zining; Xu, Yang; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 10 Word class flexibility: A deep contextualized approach ... Li, Bai; Thomas, Guillaume; Xu, Yang. - : arXiv, 2020 BASE Show details 11 To BERT or Not To BERT: Comparing Speech and Language-based Approaches for Alzheimer's Disease Detection ... Balagopalan, Aparna; Eyre, Benjamin; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 12 An information theoretic view on selecting linguistic probes ... Zhu, Zining; Rudzicz, Frank. - : arXiv, 2020 BASE Show details 13 Examining the rhetorical capacities of neural language models ... Zhu, Zining; Pan, Chuer; Abdalla, Mohamed. - : arXiv, 2020 BASE Show details 14 A textual analysis of US corporate social responsibility reports Clarkson, Peter M.; Ponn, Jordan; Richardson, Gordon D.. - : Wiley-Blackwell, 2020 BASE Show details 15 Lexical Features Are More Vulnerable, Syntactic Features Have More Predictive Power ... Novikova, Jekaterina; Balagopalan, Aparna; Shkaruta, Ksenia; Rudzicz, Frank. - : arXiv, 2019 Abstract: Understanding the vulnerability of linguistic features extracted from noisy text is important for both developing better health text classification models and for interpreting vulnerabilities of natural language models. In this paper, we investigate how generic language characteristics, such as syntax or the lexicon, are impacted by artificial text alterations. The vulnerability of features is analysed from two perspectives: (1) the level of feature value change, and (2) the level of change of feature predictive power as a result of text modifications. We show that lexical features are more sensitive to text modifications than syntactic ones. However, we also demonstrate that these smaller changes of syntactic features have a stronger influence on classification performance downstream, compared to the impact of changes to lexical features. Results are validated across three datasets representing different text-classification tasks, with different levels of lexical and syntactic complexity of both ... : EMNLP Workshop on Noisy User-generated Text (W-NUT 2019) ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1910.00065 https://dx.doi.org/10.48550/arxiv.1910.00065 BASE Hide details 16 Representation Learning for Discovering Phonemic Tone Contours ... Li, Bai; Xie, Jing Yi; Rudzicz, Frank. - : arXiv, 2019 BASE Show details 17 Machine learning for MEG during speech tasks Kostas, Demetres; Pang, Elizabeth W.; Rudzicz, Frank. - : Nature Publishing Group UK, 2019 BASE Show details 18 The Effect of Heterogeneous Data for Alzheimer's Disease Detection from Speech ... Balagopalan, Aparna; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details 19 Detecting cognitive impairments by agreeing on interpretations of linguistic features ... Zhu, Zining; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details 20 Deconfounding age effects with fair representation learning when assessing dementia ... Zhu, Zining; Novikova, Jekaterina; Rudzicz, Frank. - : arXiv, 2018 BASE Show details Page: 1 2 3

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