Au sujet de ce document
- sl:arxiv_author :
- sl:arxiv_firstAuthor : Christopher Schröder
- sl:arxiv_num : 2008.07267
- sl:arxiv_published : 2020-08-17T12:53:20Z
- sl:arxiv_summary : Natural language processing (NLP) and neural networks (NNs) have both
undergone significant changes in recent years. For active learning (AL)
purposes, NNs are, however, less commonly used -- despite their current
popularity. By using the superior text classification performance of NNs for
AL, we can either increase a model's performance using the same amount of data
or reduce the data and therefore the required annotation efforts while keeping
the same performance. We review AL for text classification using deep neural
networks (DNNs) and elaborate on two main causes which used to hinder the
adoption: (a) the inability of NNs to provide reliable uncertainty estimates,
on which the most commonly used query strategies rely, and (b) the challenge of
training DNNs on small data. To investigate the former, we construct a taxonomy
of query strategies, which distinguishes between data-based, model-based, and
prediction-based instance selection, and investigate the prevalence of these
classes in recent research. Moreover, we review recent NN-based advances in NLP
like word embeddings or language models in the context of (D)NNs, survey the
current state-of-the-art at the intersection of AL, text classification, and
DNNs and relate recent advances in NLP to AL. Finally, we analyze recent work
in AL for text classification, connect the respective query strategies to the
taxonomy, and outline commonalities and shortcomings. As a result, we highlight
gaps in current research and present open research questions.@en
- sl:arxiv_title : A Survey of Active Learning for Text Classification using Deep Neural Networks@en
- sl:arxiv_updated : 2020-08-17T12:53:20Z
- sl:bookmarkOf : https://arxiv.org/abs/2008.07267
- sl:creationDate : 2022-09-06
- sl:creationTime : 2022-09-06T18:43:54Z
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