IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING

IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING ISAAC PERSING AND VINCENT NG

Abstract. Active learning has been successfully applied to many natural language processing tasks for obtaining annotated data in a cost-effective manner. We propose several extensions to an active learner that adopts the margin-based uncertainty sampling framework. Experimental results on a cause detection problem involving the classification of aviation safety reports demonstrate the effectiveness of our extensions.

Data and Resources

Field Value
Groups
  • AmeriGEOSS
  • National Provider
  • North America
Tags
  • amerigeo
  • amerigeoss
  • ckan
  • geo
  • geoss
  • national
  • north-america
  • united-states
isopen False
license_id us-pd
license_title us-pd
maintainer Elizabeth Foughty
maintainer_email elizabeth.a.foughty@nasa.gov
metadata_created 2025-11-29T17:55:08.940613
metadata_modified 2025-11-29T17:55:08.940617
notes IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING ISAAC PERSING AND VINCENT NG Abstract. Active learning has been successfully applied to many natural language processing tasks for obtaining annotated data in a cost-effective manner. We propose several extensions to an active learner that adopts the margin-based uncertainty sampling framework. Experimental results on a cause detection problem involving the classification of aviation safety reports demonstrate the effectiveness of our extensions.
num_resources 2
num_tags 8
title IMPROVING CAUSE DETECTION SYSTEMS WITH ACTIVE LEARNING