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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 3 4 Hits 1 – 20 of 71 1 Improving BERT Model Using Contrastive Learning for Biomedical Relation Extraction ... Su, Peng; Peng, Yifan; Vijay-Shanker, K.. - : arXiv, 2021 BASE Show details 2 iTextMine: integrated text-mining system for large-scale knowledge extraction from the literature Ren, Jia; Li, Gang; Ross, Karen. - : Oxford University Press, 2018 BASE Show details 3 An Approach to Reducing Annotation Costs for BioNLP ... Bloodgood, Michael; Vijay-Shanker, K.. - : arXiv, 2014 BASE Show details 4 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.. - : arXiv, 2014 BASE Show details 5 Rapid Adaptation of POS Tagging for Domain Specific Uses ... Miller, John E.; Bloodgood, Michael; Torii, Manabu. - : arXiv, 2014 BASE Show details 6 iSimp in BioC standard format: enhancing the interoperability of a sentence simplification system Peng, Yifan; Tudor, Catalina O.; Torii, Manabu. - : Oxford University Press, 2014 BASE Show details 7 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 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. - : Digital Repository at the University of Maryland, 2009 BASE Show details 9 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 10 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 11 An Approach to Reducing Annotation Costs for BioNLP ... Bloodgood, Michael; Vijay-Shanker, K. - : Digital Repository at the University of Maryland, 2008 BASE Show details 12 An Approach to Reducing Annotation Costs for BioNLP Bloodgood, Michael; Vijay-Shanker, K. - : Association for Computational Linguistics, 2008 BASE Show details 13 Automated extraction of tree-adjoining grammars from treebanks Chen, John; Bangalore, Srinivas; Vijay-Shanker, K. In: Natural language engineering. - Cambridge : Cambridge University Press 12 (2006) 3, 251-299 BLLDB Show details 14 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 15 Rapid Adaptation of POS Tagging for Domain Specific Uses Miller, John; Bloodgood, Michael; Torii, Manabu. - : Association for Computational Linguistics, 2006 BASE Show details 16 Automated extraction of tags from the Penn Treebank Chen, John; Vijay-Shanker, K. In: New developments in parsing technology. - Dordrecht [u.a.] : Kluwer (2004), 73-89 BLLDB Show details 17 Roots, constituents, and c-command Frank, Robert; Hagstrom, Paul; Vijay-Shanker, K. In: Theoretical approaches to universals. - Amsterdam [u.a.] : Benjamins (2002), 109-137 BLLDB Show details 18 Roots, constituents, and c-command Frank, Robert; Hagstrom, Paul; Vijay-Shanker, K. In: Theoretical approaches to universals (2002), 109-139 IDS Bibliografie zur deutschen Grammatik Show details 19 Randomized rule selection in transformation-based learning : a comparative study Carberry, Sandra; Vijay-Shanker, K.; Wilson, Andrew... In: Natural language engineering. - Cambridge : Cambridge University Press 7 (2001) 2, 99-116 BLLDB Show details 20 Primitive C-command Frank, Robert; Vijay-Shanker, K. In: Syntax. - Oxford : Wiley-Blackwell 4 (2001) 3, 164-204 BLLDB Show details Page: 1 2 3 4

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