Work

An intrinsic stopping criterion for committee-based active learning

Year: 2009

Type: conference-paper

Abstract: As supervised machine learning methods are increasingly used in language technology, the need for high-quality annotated language data becomes imminent.Active learning (AL) is a means to alleviate the burden of annotation.This paper addresses the problem of knowing when to stop the AL process without having the human annotator make an explicit decision on the matter.We propose and evaluate an intrinsic criterion for committee-based AL of named entity recognizers.

Source: Conference on Computational Natural Language Learning

DOI: 10.3115/1596374.1596398

Authors: Fredrik Olsson, Katrin Tomanek

Institution: Friedrich Schiller University Jena

Language: English

Topic: Machine Learning and Algorithms

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: bronze

OpenAlex ID: W2021894617

FWCI: 2.259

Cites: 27

Cited by: 36

Related to: 10