Active Learning Strategies Based on Text Informativeness
by Ruide Li, Yoko Yamakata, Keishi Tajima
Abstract
In this paper, we propose strategies for selecting the next item to
label in active learning for text data. Text data have several
text-specific features, such as TF-IDF vectors and document
embeddings. These features have correlation with the informativeness
of the text data, so our methods select the next item to label by
using these text-specific features. We evaluate the performance of
our strategies in two problem settings: the standard active learning
setting, where we focus on the improvement of the model accuracy, and
the learning-to-enumerate setting, where we focus on the efficiency in
enumerating all instances of a given target class. We also combine
our strategies with two existing strategies: uncertainty sampling, a
well-known strategy for active learning, and the exploitation-only
strategy, a strategy used in learning-to-enumerate problems. Our
experiment on two publicly available English text datasets show that
our method outperforms the baseline methods in both problem settings.
active learning;
learning to enumerate;
informativeness;
TF-IDF;
word embedding;
uncertainty sampling
Published in Proc. of IEEE/WIC/ACM WI-IAT, pp.32-39, Niagara Falls, Canada, 2022
(125M, about 17 min.)
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