Behavioral factors in interactive training of text classifiers

Authors:
Burr Settles;Xiaojin Zhu
Affiliations:
Carnegie Mellon University, Pittsburgh PA;University of Wisconsin, Madison WI
Venue:
NAACL HLT '12 Proceedings of the 2012 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
Year:
2012

Citing 7
Cited 0

Text Classification from Labeled and Unlabeled Documents using EM

Machine Learning - Special issue on information retrieval
Learning from labeled features using generalized expectation criteria

Proceedings of the 31st annual international ACM SIGIR conference on Research and development in information retrieval
Curriculum learning

ICML '09 Proceedings of the 26th Annual International Conference on Machine Learning
Sentiment analysis of blogs by combining lexical knowledge with text classification

Proceedings of the 15th ACM SIGKDD international conference on Knowledge discovery and data mining
Active learning by labeling features

EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
A unified approach to active dual supervision for labeling features and examples

ECML PKDD'10 Proceedings of the 2010 European conference on Machine learning and knowledge discovery in databases: Part I
Closing the loop: fast, interactive semi-supervised annotation with queries on features and instances

EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing

Quantified Score

Hi-index	0.00

Visualization

Abstract

This paper describes a user study where humans interactively train automatic text classifiers. We attempt to replicate previous results using multiple "average" Internet users instead of a few domain experts as annotators. We also analyze user annotation behaviors to find that certain labeling actions have an impact on classifier accuracy, drawing attention to the important role these behavioral factors play in interactive learning systems.