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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 25 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 Abstract: Many important forms of data are stored digitally in XML format. Errors can occur in the textual content of the data in the fields of the XML. Fixing these errors manually is time-consuming and expensive, especially for large amounts of data. There is increasing interest in the research, development, and use of automated techniques for assisting with data cleaning. Electronic dictionaries are an important form of data frequently stored in XML format that frequently have errors introduced through a mixture of manual typographical entry errors and optical character recognition errors. In this paper we describe methods for flagging statistical anomalies as likely errors in electronic dictionaries stored in XML format. We describe six systems based on different sources of information. The systems detect errors using various signals in the data including uncommon characters, text length, character-based language models, word-based language models, tied-field length ratios, and tied-field transliteration models. Four of the systems detect errors based on expectations automatically inferred from content within elements of a single field type. We call these single-field systems. Two of the systems detect errors based on correspondence expectations automatically inferred from content within elements of multiple related field types. We call these tied-field systems. For each system, we provide an intuitive analysis of the type of error that it is successful at detecting. Finally, we describe two larger-scale evaluations using crowdsourcing with Amazon’s Mechanical Turk platform and using the annotations of a domain expert. The evaluations consistently show that the systems are useful for improving the efficiency with which errors in XML electronic dictionaries can be detected. Keyword: Amazon Mechanical Turk; anomaly detection; artificial intelligence; computational linguistics; computer science; crowdsourcing; data cleaning; data cleansing; databases; digital dictionaries; electronic lexicography; error detection; human language technology; machine learning; natural language processing; Optical Character Recognition; semantic computing; statistical methods; text processing; XML URL: https://doi.org/10.13016/M2RT7D http://ieeexplore.ieee.org/xpl/articleDetails.jsp?arnumber=7439308 http://hdl.handle.net/1903/17459 BASE Hide details 5 Translation memory retrieval methods Bloodgood, Michael; Strauss, Benjamin. - : Association for Computational Linguistics, 2014 BASE Show details 6 Analysis of Stopping Active Learning based on Stabilizing Predictions Bloodgood, Michael; Grothendieck, John. - : Association for Computational Linguistics, 2013 BASE Show details 7 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 8 Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : Association for Computational Linguistics, 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 Recent Advances in Computational Linguistics Yulia Ledeneva; Santiago Tianguistenco; Grigori Sidorov In: http://www.informatica.si/PDF/34-1/01_Lendeva%20-%20Recent%20Advances%20in%20Computational%20Linguistics.pdf (2009) BASE Show details 17 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 18 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 19 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 20 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 Page: 1 2

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