Bayer, Markus ; Kaufhold, Marc-André ; Buchhold, Björn ; Keller, Marcel ; Dallmeyer, Jörg ; Reuter, Christian (2022)
Data augmentation in natural language processing: a novel text generation approach for long and short text classifiers.
In: International Journal of Machine Learning and Cybernetics, 2021
doi: 10.26083/tuprints-00022164
Artikel, Zweitveröffentlichung, Verlagsversion
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Kurzbeschreibung (Abstract)
In many cases of machine learning, research suggests that the development of training data might have a higher relevance than the choice and modelling of classifiers themselves. Thus, data augmentation methods have been developed to improve classifiers by artificially created training data. In NLP, there is the challenge of establishing universal rules for text transformations which provide new linguistic patterns. In this paper, we present and evaluate a text generation method suitable to increase the performance of classifiers for long and short texts. We achieved promising improvements when evaluating short as well as long text tasks with the enhancement by our text generation method. Especially with regard to small data analytics, additive accuracy gains of up to 15.53% and 3.56% are achieved within a constructed low data regime, compared to the no augmentation baseline and another data augmentation technique. As the current track of these constructed regimes is not universally applicable, we also show major improvements in several real world low data tasks (up to +4.84 F1-score). Since we are evaluating the method from many perspectives (in total 11 datasets), we also observe situations where the method might not be suitable. We discuss implications and patterns for the successful application of our approach on different types of datasets.
| Typ des Eintrags: | Artikel |
|---|---|
| Erschienen: | 2022 |
| Autor(en): | Bayer, Markus ; Kaufhold, Marc-André ; Buchhold, Björn ; Keller, Marcel ; Dallmeyer, Jörg ; Reuter, Christian |
| Art des Eintrags: | Zweitveröffentlichung |
| Titel: | Data augmentation in natural language processing: a novel text generation approach for long and short text classifiers |
| Sprache: | Englisch |
| Publikationsjahr: | 2022 |
| Ort: | Darmstadt |
| Publikationsdatum der Erstveröffentlichung: | 2021 |
| Verlag: | Springer |
| Titel der Zeitschrift, Zeitung oder Schriftenreihe: | International Journal of Machine Learning and Cybernetics |
| Kollation: | 16 Seiten |
| DOI: | 10.26083/tuprints-00022164 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/22164 |
| Zugehörige Links: | |
| Herkunft: | Zweitveröffentlichungsservice |
| Kurzbeschreibung (Abstract): | In many cases of machine learning, research suggests that the development of training data might have a higher relevance than the choice and modelling of classifiers themselves. Thus, data augmentation methods have been developed to improve classifiers by artificially created training data. In NLP, there is the challenge of establishing universal rules for text transformations which provide new linguistic patterns. In this paper, we present and evaluate a text generation method suitable to increase the performance of classifiers for long and short texts. We achieved promising improvements when evaluating short as well as long text tasks with the enhancement by our text generation method. Especially with regard to small data analytics, additive accuracy gains of up to 15.53% and 3.56% are achieved within a constructed low data regime, compared to the no augmentation baseline and another data augmentation technique. As the current track of these constructed regimes is not universally applicable, we also show major improvements in several real world low data tasks (up to +4.84 F1-score). Since we are evaluating the method from many perspectives (in total 11 datasets), we also observe situations where the method might not be suitable. We discuss implications and patterns for the successful application of our approach on different types of datasets. |
| Freie Schlagworte: | Textual data augmentation, Small text data analytics, Text generation, Long and short text classifier |
| Status: | Verlagsversion |
| URN: | urn:nbn:de:tuda-tuprints-221643 |
| Sachgruppe der Dewey Dezimalklassifikatin (DDC): | 000 Allgemeines, Informatik, Informationswissenschaft > 004 Informatik |
| Fachbereich(e)/-gebiet(e): | 20 Fachbereich Informatik 20 Fachbereich Informatik > Wissenschaft und Technik für Frieden und Sicherheit (PEASEC) Forschungsfelder Forschungsfelder > Information and Intelligence Forschungsfelder > Information and Intelligence > Cybersecurity & Privacy |
| Hinterlegungsdatum: | 05 Sep 2022 13:19 |
| Letzte Änderung: | 07 Sep 2022 09:08 |
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| Suche nach Titel in: | TUfind oder in Google |
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