Date
2019Subject
004 Data processing and computer science Text MiningKlassifikationFramework <Informatik>Natürlichsprachiges SystemMetadata
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Konferenzveröffentlichung
Towards A Taxonomy of Text Mining Features
Abstract
Recently, text mining has received special attention from both researchers and practitioners, since it enables the development of intelligent and automated services. Text mining has been influenced by different disciplines like computer science, statistics, computational linguistics and library and information sciences. However, text mining features that evolved in one particular discipline are often unknown or rarely used in the other disciplines. No scientific feature framework exits which facilitates costly feature engineering and evaluation. Therefore, we aim to develop a novel text mining feature taxonomy, which helps researchers and practitioners to develop, refine, compare and evaluate their text mining studies. In this research in progress paper, we focus on laying the foundation for our taxonomy development by presenting our first two research cycles. Here, we were aiming for diversity, not completeness. We derived five dimensions and classified different text features accordingly to provide a deeper understanding.
Citation
In: European Conference on Information Systems (Hrsg.): Proceedings of the 27th European Conference on Information Systems (ECIS). Association for Information Systems: 2019, S. ; ISBN 978-1-7336325-0-8Citation
@inproceedings{doi:10.17170/kobra-202104153672,
author={Fromm, Hansjörg and Wambsganss, Thiemo and Söllner, Matthias},
title={Towards A Taxonomy of Text Mining Features},
booktitle={Proceedings of the 27th European Conference on Information Systems (ECIS)},
year={2019}
}
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2021-04-28T08:49:14Z 2021-04-28T08:49:14Z 2019 doi:10.17170/kobra-202104153672 http://hdl.handle.net/123456789/12749 eng Association for Information Systems Urheberrechtlich geschützt https://rightsstatements.org/page/InC/1.0/ feature engineering text mining taxonomy natural language processing 004 Towards A Taxonomy of Text Mining Features Konferenzveröffentlichung Recently, text mining has received special attention from both researchers and practitioners, since it enables the development of intelligent and automated services. Text mining has been influenced by different disciplines like computer science, statistics, computational linguistics and library and information sciences. However, text mining features that evolved in one particular discipline are often unknown or rarely used in the other disciplines. No scientific feature framework exits which facilitates costly feature engineering and evaluation. Therefore, we aim to develop a novel text mining feature taxonomy, which helps researchers and practitioners to develop, refine, compare and evaluate their text mining studies. In this research in progress paper, we focus on laying the foundation for our taxonomy development by presenting our first two research cycles. Here, we were aiming for diversity, not completeness. We derived five dimensions and classified different text features accordingly to provide a deeper understanding. open access Fromm, Hansjörg Wambsganss, Thiemo Söllner, Matthias Text Mining Klassifikation Framework <Informatik> Natürlichsprachiges System publishedVersion 2019-06 Stockholm & Uppsala, Sweden Proceedings of the 27th European Conference on Information Systems (ECIS) European Conference on Information Systems ISBN 978-1-7336325-0-8 false 27th European Conference on Information Systems (ECIS) Research in Progress Paper 53
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