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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 – 7 of 7 1 Multi-Annotator Modeling to Encode Diverse Perspectives in Hate Speech Annotations ... 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; Díaz, Mark; Mostafazadeh Davani, Aida. - : Underline Science Inc., 2021 BASE Show details 2 Learning to Recognize Dialect Features ... NAACL 2021 2021; Clark, Jonathan; Demszky, Dorottya. - : Underline Science Inc., 2021 BASE Show details 3 Learning to Recognize Dialect Features ... Demszky, Dorottya; Sharma, Devyani; Clark, Jonathan H.. - : arXiv, 2020 BASE Show details 4 Perturbation Sensitivity Analysis to Detect Unintended Model Biases ... Prabhakaran, Vinodkumar; Hutchinson, Ben; Mitchell, Margaret. - : arXiv, 2019 Abstract: Data-driven statistical Natural Language Processing (NLP) techniques leverage large amounts of language data to build models that can understand language. However, most language data reflect the public discourse at the time the data was produced, and hence NLP models are susceptible to learning incidental associations around named referents at a particular point in time, in addition to general linguistic meaning. An NLP system designed to model notions such as sentiment and toxicity should ideally produce scores that are independent of the identity of such entities mentioned in text and their social associations. For example, in a general purpose sentiment analysis system, a phrase such as I hate Katy Perry should be interpreted as having the same sentiment as I hate Taylor Swift. Based on this idea, we propose a generic evaluation framework, Perturbation Sensitivity Analysis, which detects unintended model biases related to named entities, and requires no new annotations or corpora. We demonstrate the ... : EMNLP 2019 Short Paper ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1910.04210 https://arxiv.org/abs/1910.04210 BASE Hide details 5 Author Commitment and Social Power: Automatic Belief Tagging to Infer the Social Context of Interactions ... Prabhakaran, Vinodkumar; Ganeshkumar, Premkumar; Rambow, Owen. - : arXiv, 2018 BASE Show details 6 Statistical modality tagging from rule-based annotations and crowdsourcing ... Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : arXiv, 2015 BASE Show details 7 Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : Association for Computational Linguistics, 2012 BASE Show details

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