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
Article, Secondary publication, Publisher's Version
 | There is a more recent version of this item available. |
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.
| Item Type: | Article |
|---|---|
| Erschienen: | 2022 |
| Creators: | Bayer, Markus ; Kaufhold, Marc-André ; Buchhold, Björn ; Keller, Marcel ; Dallmeyer, Jörg ; Reuter, Christian |
| Type of entry: | Secondary publication |
| Title: | Data augmentation in natural language processing: a novel text generation approach for long and short text classifiers |
| Language: | English |
| Date: | 2022 |
| Place of Publication: | Darmstadt |
| Year of primary publication: | 2021 |
| Publisher: | Springer |
| Journal or Publication Title: | International Journal of Machine Learning and Cybernetics |
| Collation: | 16 Seiten |
| DOI: | 10.26083/tuprints-00022164 |
| URL / URN: | https://tuprints.ulb.tu-darmstadt.de/22164 |
| Corresponding Links: | |
| Origin: | Secondary publication service |
| 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. |
| Uncontrolled Keywords: | Textual data augmentation, Small text data analytics, Text generation, Long and short text classifier |
| Status: | Publisher's Version |
| URN: | urn:nbn:de:tuda-tuprints-221643 |
| Classification DDC: | 000 Generalities, computers, information > 004 Computer science |
| Divisions: | 20 Department of Computer Science 20 Department of Computer Science > Science and Technology for Peace and Security (PEASEC) Forschungsfelder Forschungsfelder > Information and Intelligence Forschungsfelder > Information and Intelligence > Cybersecurity & Privacy |
| Date Deposited: | 05 Sep 2022 13:19 |
| Last Modified: | 07 Sep 2022 09:08 |
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| Suche nach Titel in: | TUfind oder in Google |
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- Data augmentation in natural language processing: a novel text generation approach for long and short text classifiers. (deposited 05 Sep 2022 13:19) [Currently Displayed]
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