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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 – 17 of 17 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 A random forest system combination approach for error detection in digital dictionaries Rodrigues, Paul; Zajic, David; Doermann, David; Bloodgood, Michael; Ye, Peng. - : Association for Computational Linguistics, 2012 Abstract: When digitizing a print bilingual dictionary, whether via optical character recognition or manual entry, it is inevitable that errors are introduced into the electronic version that is created. We investigate automating the process of detecting errors in an XML representation of a digitized print dictionary using a hybrid approach that combines rule-based, feature-based, and language model-based methods. We investigate combining methods and show that using random forests is a promising approach. We find that in isolation, unsupervised methods rival the performance of supervised methods. Random forests typically require training data so we investigate how we can apply random forests to combine individual base methods that are themselves unsupervised without requiring large amounts of training data. Experiments reveal empirically that a relatively small amount of data is sufficient and can potentially be further reduced through specific selection criteria. Keyword: artificial intelligence; bilingual dictionaries; computational linguistics; computer science; digital dictionaries; electronic lexicography; error detection; human language technology; hybrid systems; machine learning; natural language processing; random forests; system combination URL: http://hdl.handle.net/1903/15085 BASE Hide details 6 Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : Association for Computational Linguistics, 2012 BASE Show details 7 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 8 Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach Baker, Kathryn; Piatko, Christine; Miller, Scott. - 2010 BASE Show details 9 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 10 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 11 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 12 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 13 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 14 An Approach to Reducing Annotation Costs for BioNLP ... Bloodgood, Michael; Vijay-Shanker, K. - : Digital Repository at the University of Maryland, 2008 BASE Show details 15 An Approach to Reducing Annotation Costs for BioNLP Bloodgood, Michael; Vijay-Shanker, K. - : Association for Computational Linguistics, 2008 BASE Show details 16 Rapid Adaptation of POS Tagging for Domain Specific Uses ... Miller, John; Bloodgood, Michael; Torii, Manabu. - : Digital Repository at the University of Maryland, 2006 BASE Show details 17 Rapid Adaptation of POS Tagging for Domain Specific Uses Miller, John; Bloodgood, Michael; Torii, Manabu. - : Association for Computational Linguistics, 2006 BASE Show details

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