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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 – 8 of 8 1 From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains ... Mather, Brodie; Dorr, Bonnie J; Dalton, Adam. - : arXiv, 2022 BASE Show details 2 Detecting Asks in SE attacks: Impact of Linguistic and Structural Knowledge ... Dorr, Bonnie J.; Bhatia, Archna; Dalton, Adam. - : arXiv, 2020 BASE Show details 3 Adaptation of a Lexical Organization for Social Engineering Detection and Response Generation ... Bhatia, Archna; Dalton, Adam; Mather, Brodie. - : arXiv, 2020 BASE Show details 4 Statistical modality tagging from rule-based annotations and crowdsourcing ... Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : arXiv, 2015 BASE Show details 5 Use of Modality and Negation in Semantically-Informed Syntactic MT ... Baker, Kathryn; Bloodgood, Michael; Dorr, Bonnie J.; Callison-Burch, Chris; Filardo, Nathaniel W.; Piatko, Christine; Levin, Lori; Miller, Scott. - : arXiv, 2015 Abstract: This paper describes the resource- and system-building efforts of an eight-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Language Exploration (SCALE-2009) on Semantically-Informed Machine Translation (SIMT). We describe a new modality/negation (MN) annotation scheme, the creation of a (publicly available) MN lexicon, and two automated MN taggers that we built using the annotation scheme and lexicon. Our annotation scheme isolates three components of modality and negation: a trigger (a word that conveys modality or negation), a target (an action associated with modality or negation) and a holder (an experiencer of modality). We describe how our MN lexicon was semi-automatically produced and we demonstrate that a structure-based MN tagger results in precision around 86% (depending on genre) for tagging of a standard LDC data set. We apply our MN annotation scheme to statistical machine translation using a syntactic framework that supports the inclusion of ... : 28 pages, 13 figures, 2 tables; appeared in Computational Linguistics, 38(2):411-438, 2012 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences; I.2.7; I.2.6; I.5.1; I.5.4; Machine Learning cs.LG; Machine Learning stat.ML URL: https://arxiv.org/abs/1502.01682 https://dx.doi.org/10.48550/arxiv.1502.01682 BASE Hide details 6 A Modality Lexicon and its use in Automatic Tagging ... Baker, Kathryn; Bloodgood, Michael; Dorr, Bonnie J.. - : arXiv, 2014 BASE Show details 7 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ... Baker, Kathryn; Bloodgood, Michael; Callison-Burch, Chris. - : arXiv, 2014 BASE Show details 8 Computing Lexical Contrast ... Mohammad, Saif M.; Dorr, Bonnie J.; Hirst, Graeme. - : arXiv, 2013 BASE Show details

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