20 citations · 38 across the 6 of their papers we have counts for
6 papers
Analysis of Stopping Active Learning based on Stabilizing Predictions
Michael Bloodgood, John Grothendieck
Within the natural language processing (NLP) community, active learning has been widely investigated and applied in order to alleviate the annotation bottleneck faced by developers…
Use of Modality and Negation in Semantically-Informed Syntactic MT
Kathryn Baker, Michael Bloodgood, Bonnie J. Dorr +5
This paper describes the resource- and system-building efforts of an eight-week Johns Hopkins University Human Language Technology Center of Excellence Summer Camp for Applied Lang…
Annotating Cognates and Etymological Origin in Turkic Languages
Benjamin S. Mericli, Michael Bloodgood
Turkic languages exhibit extensive and diverse etymological relationships among lexical items. These relationships make the Turkic languages promising for exploring automated trans…
A Method for Stopping Active Learning Based on Stabilizing Predictions and the Need for User-Adjustable Stopping
Michael Bloodgood, K. Vijay-Shanker
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 stabl…
Taking into Account the Differences between Actively and Passively Acquired Data: The Case of Active Learning with Support Vector Machines for Imbalanced Datasets
Michael Bloodgood, K. Vijay-Shanker
Actively sampled data can have very different characteristics than passively sampled data. Therefore, it's promising to investigate using different inference procedures during AL t…
An Approach to Reducing Annotation Costs for BioNLP
Michael Bloodgood, K. Vijay-Shanker
There is a broad range of BioNLP tasks for which active learning (AL) can significantly reduce annotation costs and a specific AL algorithm we have developed is particularly effect…