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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. - : MIT Press, 2012 BASE Show 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 Abstract: A survey of existing methods for stopping active learning (AL) reveals the needs for methods that are: more widely applicable; more aggressive in saving annotations; and more stable across changing datasets. A new method for stopping AL based on stabilizing predictions is presented that addresses these needs. Furthermore, stopping methods are required to handle a broad range of different annotation/performance tradeoff valuations. Despite this, the existing body of work is dominated by conservative methods with little (if any) attention paid to providing users with control over the behavior of stopping methods. The proposed method is shown to fill a gap in the level of aggressiveness available for stopping AL and supports providing users with control over stopping behavior. ... Keyword: active learning; aggressive stopping; agreement metrics; agreement statistics; annotation bottleneck; annotation costs; annotation/performance tradeoff; artificial intelligence; binary classification; biomedical named entity classification; Cohen's Kappa; computational linguistics; computer science; conservative stopping; contingency table analysis; F-measure; F-score; human language technology; inter-model agreement; Kappa statistic; machine learning; named entity classification; natural language processing; query learning; selective sampling; stabilizing predictions; statistical methods; stop set; stop set construction; stopping criteria; stopping methods; support vector machines; SVMs; text classification; text processing; user-adjustable stopping URL: https://dx.doi.org/10.13016/m25p4m http://hdl.handle.net/1903/15596 BASE Hide 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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