PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

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.

Abstract

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.

PDF - PASCAL Members only - Requires Adobe Acrobat Reader or other PDF viewer.
EPrint Type:Conference or Workshop Item (Talk)
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Information Retrieval & Textual Information Access
ID Code:2512
Deposited By:Anastasia Krithara
Deposited On:22 November 2006