Active, Semi-Supervised Learning for Textual Information Access
Anastasia Krithara, Cyril Goutte, Massih Amini and Jean-Michel Renders
In: International Workshop on Intelligent Information Access, 06-08 July 2006, Helsinki, Finland.
Machine learning techniques have been used for various
tasks of document management and textual information
access, such as categorisation, information extraction,
or automatic organization of large document collections.
Acquiring the annotated data necessary to apply supervised
learning techniques is a major challenge for text applications,
especially in very large collections. Annotating textual data
usually requires humans who can read and understand the
texts, and is therefore very costly, especially in technical
domains. In this contribution, we address the problem or
reducing this annotation burden.