20 citations · 60 across the 5 of their papers we have counts for
6 papers
Early Forecasting of Text Classification Accuracy and F-Measure with Active Learning
Thomas Orth, Michael Bloodgood
When creating text classification systems, one of the major bottlenecks is the annotation of training data. Active learning has been proposed to address this bottleneck using stopp…
The Use of Unlabeled Data versus Labeled Data for Stopping Active Learning for Text Classification
Garrett Beatty, Ethan Kochis, Michael Bloodgood
Annotation of training data is the major bottleneck in the creation of text classification systems. Active learning is a commonly used technique to reduce the amount of training da…
Stopping Active Learning based on Predicted Change of F Measure for Text Classification
Michael Altschuler, Michael Bloodgood
During active learning, an effective stopping method allows users to limit the number of annotations, which is cost effective. In this paper, a new stopping method called Predicted…
Acquisition of Translation Lexicons for Historically Unwritten Languages via Bridging Loanwords
Michael Bloodgood, Benjamin Strauss
With the advent of informal electronic communications such as social media, colloquial languages that were historically unwritten are being written for the first time in heavily co…
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…