2023
doi: 10.12720/jait.14.4.796-802
Linguistic Driven Feature Selection for Text Classification as Stop Word Replacement
Daniel Schönle1,*
,
Christoph Reich1 ,
Djaffar Ould Abdeslam2
- 1Das Institut für Data Science, Cloud Computing und IT-Sicherheit (IDACUS), Furtwangen University, Furtwangen, Germany
- 2L’Institut de Recherche en Informatique, Mathématiques, Automatique et Signal (IRIMAS), Université de Haute Alsace, Mulhouse, France
- Manuscript receivedOctober 8, 2022
- revisedDecember 27, 2022
- acceptedJanuary 30, 2023
- publishedAugust 11, 2023
Abstract
The common corpus optimization method “stop words removal” is based on the assumption that text tokens with high occurrence frequency can be removed without affecting classification performance. Linguistic information regarding sentence structure is ignored as well as preferences of the classification technology. We propose the Weighted Unimportant Part-of-Speech Model (WUP-Model) for token removal in the pre-processing of text corpora. The weighted relevance of a token is determined using classification relevance and classification performance impact. The WUP-Model uses linguistic information (part of speech) as grouping criteria. Analogous to stop word removal, we provide a set of irrelevant part of speech (WUP-Instance) for word removal. In a proof-of-concept we created WUP-Instances for several classification algorithms. The evaluation showed significant advantages compared to classic stop word removal. The tree-based classifier increased runtime by 65% and 25% in performance. The performance of the other classifiers decreased between 0.2% and 2.4%, their runtime improved between −4.4% and −24.7%. These results prove beneficial effects of the proposed WUP-Model.
Keywords
- text classification
- natural language processing
- feature selection
- linguistics
How to Cite
Daniel Schönle, Christoph Reich, and Djaffar Ould Abdeslam, "Linguistic Driven Feature Selection for Text Classification as Stop Word Replacement," Journal of Advances in Information Technology, vol. 14, no. 4, pp. 796-802, 2023. https://doi.org/10.12720/jait.14.4.796-802
Copyright & License
Copyright © 2023 by the authors. This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).
