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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 Hits 1 – 20 of 23 1 Filtering Tweets for Social Unrest ... Mishler, Alan; Wonus, Kevin; Chambers, Wendy. - : Digital Repository at the University of Maryland, 2017 BASE Show details 2 Filtering Tweets for Social Unrest Mishler, Alan; Wonus, Kevin; Chambers, Wendy. - : IEEE, 2017 BASE Show details 3 Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection ... Bloodgood, Michael; Strauss, Benjamin. - : Digital Repository at the University of Maryland, 2016 BASE Show details 4 Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection Bloodgood, Michael; Strauss, Benjamin. - : IEEE, 2016 BASE Show details 5 Translation memory retrieval methods Bloodgood, Michael; Strauss, Benjamin. - : Association for Computational Linguistics, 2014 BASE Show details 6 A random forest system combination approach for error detection in digital dictionaries Rodrigues, Paul; Zajic, David; Doermann, David. - : Association for Computational Linguistics, 2012 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 8 Annotating Cognates and Etymological Origin in Turkic Languages Bloodgood, Michael; Mericli, Benjamin. - : European Language Resources Association, 2012 BASE Show details 9 Use of Modality and Negation in Semantically-Informed Syntactic MT Bloodgood, Michael; Filardo, Nathaniel; Levin, Lori; Dorr, Bonnie; Baker, Kathryn; Piatko, Christine; Miller, Scott; Callison-Burch, Chris. - : MIT Press, 2012 Abstract: This article describes the resource- and system-building efforts of an 8-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 semantic annotations. Syntactic tags enriched with semantic annotations are assigned to parse trees in the target-language training texts through a process of tree grafting. Although the focus of our work is modality and negation, the tree grafting procedure is general and supports other types of semantic information. We exploit this capability by including named entities, produced by a pre-existing tagger, in addition to the MN elements produced by the taggers described here. The resulting system significantly outperformed a linguistically naive baseline model (Hiero), and reached the highest scores yet reported on the NIST 2009 Urdu–English test set. This finding supports the hypothesis that both syntactic and semantic information can improve translation quality. ; This work was supported, in part, by the Johns Hopkins Human Language Technology Center of Excellence (HLTCOE), by the National Science Foundation under grant IIS-0713448, and by BBN Technologies under GALE DARPA/IPTO contract no. HR0011-06-C-0022. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the sponsor. Keyword: artificial intelligence; computational linguistics; computer science; human language technology; machine translation; modality; natural language processing; negation; semantically-informed machine translation; statistical machine translation; statistical methods; translation technology URL: http://hdl.handle.net/1903/15547 BASE Hide details 10 Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling ... Rodrigues, Paul; Zajic, David; Doermann, David. - : Digital Repository at the University of Maryland, 2011 BASE Show details 11 Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling Bloodgood, Michael; Ye, Peng; Rodrigues, Paul. - : Trojina Institute for Applied Slovene Studies, 2011 BASE Show details 12 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach ... Baker, Kathryn; Bloodgood, Michael; Callison-Burch, Chris. - : Digital Repository at the University of Maryland, 2010 BASE Show details 13 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach Baker, Kathryn; Piatko, Christine; Miller, Scott. - 2010 BASE Show details 14 Using Mechanical Turk to Build Machine Translation Evaluation Sets Bloodgood, Michael; Callison-Burch, Chris. - : Association for Computational Linguistics, 2010 BASE Show details 15 Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation Bloodgood, Michael; Callison-Burch, Chris. - : Association for Computational Linguistics, 2010 BASE Show details 16 A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping ... Bloodgood, Michael; Vijay-Shanker, K. - : Digital Repository at the University of Maryland, 2009 BASE Show details 17 Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets ... Bloodgood, Michael; Vijay-Shanker, K. - : Digital Repository at the University of Maryland, 2009 BASE Show details 18 A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping Bloodgood, Michael; Vijay-Shanker, K. - : Association for Computational Linguistics, 2009 BASE Show details 19 Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets Bloodgood, Michael; Vijay-Shanker, K. - : Association for Computational Linguistics, 2009 BASE Show details 20 An Approach to Reducing Annotation Costs for BioNLP ... Bloodgood, Michael; Vijay-Shanker, K. - : Digital Repository at the University of Maryland, 2008 BASE Show details Page: 1 2

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