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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
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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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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7
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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8
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
Abstract: Actively sampled data can have very different characteristics than passively sampled data. Therefore, it’s promising to investigate using different inference procedures during AL than are used during passive learning (PL). This general idea is explored in detail for the focused case of AL with cost-weighted SVMs for imbalanced data, a situation that arises for many HLT tasks. The key idea behind the proposed InitPA method for addressing imbalance is to base cost models during AL on an estimate of overall corpus imbalance computed via a small unbiased sample rather than the imbalance in the labeled training data, which is the leading method used during PL.
Keyword: active learning; annotation bottleneck; annotation costs; artificial intelligence; asymmetric cost factors; asymmetric cost weights; binary classification; biomedical natural language processing; biomedical text processing; BioNLP; computational linguistics; computer science; corpus imbalance; cost modeling; cost models; cost-sensitive active learning; cost-sensitive learning; cost-weighted support vector machines; cost-weighted SVMs; human language technology; imbalanced data; imbalanced datasets; imbalanced learning; machine learning; natural language processing; newswire text classification; positive amplification; protein-protein interaction extraction; query learning; relation extraction; selective sampling; statistical methods; support vector machines; SVMs; text classification; text processing
URL: http://hdl.handle.net/1903/15585
https://doi.org/10.13016/M2QP4K
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9
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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10
An Approach to Reducing Annotation Costs for BioNLP
Bloodgood, Michael; Vijay-Shanker, K. - : Association for Computational Linguistics, 2008
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11
Rapid Adaptation of POS Tagging for Domain Specific Uses ...
Miller, John; Bloodgood, Michael; Torii, Manabu. - : Digital Repository at the University of Maryland, 2006
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12
Rapid Adaptation of POS Tagging for Domain Specific Uses
Miller, John; Bloodgood, Michael; Torii, Manabu. - : Association for Computational Linguistics, 2006
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