Email this article An analysis of intelligent methods and algorithms for unlabeled data processing
- Authors: Engel EА1, Engel' E.A.1
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Affiliations:
- Issue: Vol 12, No 2 (2011)
- Pages: 92-96
- Section: Articles
- URL: https://journals.eco-vector.com/2712-8970/article/view/505744
- ID: 505744
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Abstract
Intelligent algorithms and method is well-suited to many problems in data processing, where unlabeled data may
be abundant. We survey previously used selection strategies for intelligent model, and propose two novel algorithms
to address their shortcomings, focus on Active Learning (AL). While has already been shown to markedly reduce
the annotation efforts for many sequence labeling tasks compared to random selection, AL remains unconcerned about
the internal structure of the selected sequences (typically, sentences). We propose a semi-supervised AL approach
for sequence labeling.
be abundant. We survey previously used selection strategies for intelligent model, and propose two novel algorithms
to address their shortcomings, focus on Active Learning (AL). While has already been shown to markedly reduce
the annotation efforts for many sequence labeling tasks compared to random selection, AL remains unconcerned about
the internal structure of the selected sequences (typically, sentences). We propose a semi-supervised AL approach
for sequence labeling.
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