Nonlinguistic Information Extraction by Semi-Supervised Techniques

Maria Semenkina, Shakhnaz Akhmedova, Eugene Semenkin

2017

Abstract

The concept of nonlinguistic information includes all types of extra linguistic information such as factors of age, emotion and physical states, accent and others. Semi-supervised techniques based on using both labelled and unlabelled examples can be an efficient tool for solving nonlinguistic information extraction problems with large amounts of unlabelled data. In this paper a new cooperation of biology related algorithms (COBRA) for semi-supervised support vector machines (SVM) training and a new self-configuring genetic algorithm (SelfCGA) for the automated design of semi-supervised artificial neural networks (ANN) are presented. Firstly, the performance and behaviour of the proposed semi-supervised SVMs and semi-supervised ANNs were studied under common experimental settings; and their workability was established. Then their efficiency was estimated on a speech-based emotion recognition problem.

Paper Citation

in Harvard Style

Semenkina M., Akhmedova S. and Semenkin E. (2017). Nonlinguistic Information Extraction by Semi-Supervised Techniques . In Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO, ISBN 978-989-758-263-9, pages 312-317. DOI: 10.5220/0006438703120317

in Bibtex Style

@conference{icinco17,
author={Maria Semenkina and Shakhnaz Akhmedova and Eugene Semenkin},
title={Nonlinguistic Information Extraction by Semi-Supervised Techniques},
booktitle={Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,},
year={2017},
pages={312-317},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0006438703120317},
isbn={978-989-758-263-9},
}

in EndNote Style

TY - CONF
JO - Proceedings of the 14th International Conference on Informatics in Control, Automation and Robotics - Volume 1: ICINCO,
TI - Nonlinguistic Information Extraction by Semi-Supervised Techniques
SN - 978-989-758-263-9
AU - Semenkina M.
AU - Akhmedova S.
AU - Semenkin E.
PY - 2017
SP - 312
EP - 317
DO - 10.5220/0006438703120317