A Sequential Algorithm for Training Text Classifiers

Thumbnail Image

Date

Authors

Journal Title

Journal ISSN

Volume Title

Publisher

Abstract

Description

The ability to cheaply train text classifiers is critical to their use in information retrieval, content analysis, natural language processing, and other tasks involving data which is partly or fully textual. An algorithm for sequential sampling during machine learning of statistical classifiers was developed and tested on a newswire text categorization task. This method, which we call uncertainty sampling, reduced by as much as 500-fold the amount of training data that would have to be manually classified to achieve a given level of effectiveness.
10 pages, uuencoded, compressed PostScript; Proc. SIGIR-94 LaTex available from lewis@research.att.com

Keywords

Computation and Language

Citation

URI

http://salesiana.dossiersoluciones.com/handle/123456789/147391

Consulte el texto completo en el siguiente enlace:

https://arxiv.org/abs/cmp-lg/9407020
 http://arxiv.org/abs/cmp-lg/9407020

Collections

arXiv