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1
Filtering Tweets for Social Unrest ...
Mishler, Alan; Wonus, Kevin; Chambers, Wendy. - : Digital Repository at the University of Maryland, 2017
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Filtering Tweets for Social Unrest
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3
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection ...
Bloodgood, Michael; Strauss, Benjamin. - : Digital Repository at the University of Maryland, 2016
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4
Data Cleaning for XML Electronic Dictionaries via Statistical Anomaly Detection
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5
Translation memory retrieval methods
Bloodgood, Michael; Strauss, Benjamin. - : Association for Computational Linguistics, 2014
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6
A random forest system combination approach for error detection in digital dictionaries
Rodrigues, Paul; Zajic, David; Doermann, David. - : Association for Computational Linguistics, 2012
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7
Statistical Modality Tagging from Rule-based Annotations and Crowdsourcing
Prabhakaran, Vinodkumar; Bloodgood, Michael; Diab, Mona. - : Association for Computational Linguistics, 2012
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8
Annotating Cognates and Etymological Origin in Turkic Languages
Bloodgood, Michael; Mericli, Benjamin. - : European Language Resources Association, 2012
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9
Use of Modality and Negation in Semantically-Informed Syntactic MT
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10
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling ...
Rodrigues, Paul; Zajic, David; Doermann, David. - : Digital Repository at the University of Maryland, 2011
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11
Detecting Structural Irregularity in Electronic Dictionaries Using Language Modeling
Bloodgood, Michael; Ye, Peng; Rodrigues, Paul; Zajic, David; Doermann, David. - : Trojina Institute for Applied Slovene Studies, 2011
Abstract: Dictionaries are often developed using tools that save to Extensible Markup Language (XML)-based standards. These standards often allow high-level repeating elements to represent lexical entries, and utilize descendants of these repeating elements to represent the structure within each lexical entry, in the form of an XML tree. In many cases, dictionaries are published that have errors and inconsistencies that are expensive to find manually. This paper discusses a method for dictionary writers to quickly audit structural regularity across entries in a dictionary by using statistical language modeling. The approach learns the patterns of XML nodes that could occur within an XML tree, and then calculates the probability of each XML tree in the dictionary against these patterns to look for entries that diverge from the norm. ; This material is based upon work supported, in whole or in part, with funding from the United States Government. Any opinions, findings and conclusions, or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the University of Maryland, College Park and/or any agency or entity of the United States Government. Nothing in this report is intended to be and shall not be treated or construed as an endorsement or recommendation by the University of Maryland, United States Government, or the authors of the product, process, or service that is the subject of this report. No one may use any information contained or based on this report in advertisements or promotional materials related to any company product, process, or service or in support of other commercial purposes.
Keyword: anomaly detection; computational linguistics; computer science; electronic dictionaries; electronic lexicography; error correction; human language technology; language modeling; natural language processing; statistical methods; XML
URL: https://doi.org/10.13016/M2WC75
http://hdl.handle.net/1903/15576
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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
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13
Semantically-Informed Syntactic Machine Translation: A Tree-Grafting Approach
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14
Using Mechanical Turk to Build Machine Translation Evaluation Sets
Bloodgood, Michael; Callison-Burch, Chris. - : Association for Computational Linguistics, 2010
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15
Bucking the Trend: Large-Scale Cost-Focused Active Learning for Statistical Machine Translation
Bloodgood, Michael; Callison-Burch, Chris. - : Association for Computational Linguistics, 2010
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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
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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
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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
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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
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20
An Approach to Reducing Annotation Costs for BioNLP ...
Bloodgood, Michael; Vijay-Shanker, K. - : Digital Repository at the University of Maryland, 2008
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