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Bootstrapping for Hierarchical Document Classification by Giordano Adami; Paolo Avesani; Diego Sona is a scholarly article available to read on EtoBox.

What is Bootstrapping for Hierarchical Document Classification about?

Managing the hierarchical organization of data is starting to play a key role in the knowledge management community due to the great amount of human resources needed to create and maintain these organized repositories of information. Machine learning community has in part addressed this problem by developing hierarchical supervised classifiers that help maintainers to categorize new resources within given hierarchies. Although such learning models succeed in exploiting relational knowledge, they are highly demanding in terms of labeled examples, because the number of categories is related to the dimension of the corresponding hierarchy. Hence, the creation of new directories or the modification of existing ones require strong investments.This paper proposes a semi-automatic process (interleaved with human suggestions) whose aim is to minimize (simplify) the work required to the administrators when creating, modifying, and maintaining directories. Within this process, bootstrapping a taxonomy with examples represents a critical factor for the effective exploitation of any supervised learning model. For this reason we propose a method for the bootstrapping 1 process that makes a firs

Author

Giordano Adami; Paolo Avesani; Diego Sona

Publisher

ACM

Published

2003

Language

EN