Automatically detecting codeswitching between Nigerian Pidgin English and English
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2026
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| author | Ogbonda, Godspower Teahan, William |
| author_facet | Ogbonda, Godspower Teahan, William |
| contents | <p>This dataset contains manually annotated ground truth files for <br>evaluating code-switching detection between Nigerian Pidgin English <br>(NPE) and Standard English. It was developed as part of a study <br>evaluating traditional machine learning classifiers, a BiLSTM deep <br>learning model, and compression-based PPM models via the Tawa toolkit.</p> <p>Two annotation formats are provided:</p> <p>ml_ground_truth: XML-tagged annotations used to evaluate traditional <br>machine learning and deep learning models, where pidgin and English <br>Segments are marked using XML-style tags.</p> <p>tawa_ground_truth: Character-level annotations used to evaluate the <br>Tawa PPM compression-based models, where P denotes Nigerian Pidgin <br>English and E denotes Standard English.</p> <p>All annotations were manually verified by three native speakers of <br>Nigerian Pidgin English. The ground truth was withheld entirely from model training to ensure unbiased <br>evaluation. Full methodological details are provided in the associated <br>publication.</p> <p>Note: This ground truth dataset is released independently of the full <br>Mixed corpus. The complete corpus cannot be publicly distributed due <br>to copyright restrictions on a subset of the source materials.</p> |
| format | Recurso digital |
| id | zenodo_https___doi_org_10_5281_zenodo_20117988 |
| institution | Zenodo |
| language | |
| publishDate | 2026 |
| publisher | Zenodo |
| record_format | zenodo |
| spellingShingle | Automatically detecting codeswitching between Nigerian Pidgin English and English Ogbonda, Godspower Teahan, William <p>This dataset contains manually annotated ground truth files for <br>evaluating code-switching detection between Nigerian Pidgin English <br>(NPE) and Standard English. It was developed as part of a study <br>evaluating traditional machine learning classifiers, a BiLSTM deep <br>learning model, and compression-based PPM models via the Tawa toolkit.</p> <p>Two annotation formats are provided:</p> <p>ml_ground_truth: XML-tagged annotations used to evaluate traditional <br>machine learning and deep learning models, where pidgin and English <br>Segments are marked using XML-style tags.</p> <p>tawa_ground_truth: Character-level annotations used to evaluate the <br>Tawa PPM compression-based models, where P denotes Nigerian Pidgin <br>English and E denotes Standard English.</p> <p>All annotations were manually verified by three native speakers of <br>Nigerian Pidgin English. The ground truth was withheld entirely from model training to ensure unbiased <br>evaluation. Full methodological details are provided in the associated <br>publication.</p> <p>Note: This ground truth dataset is released independently of the full <br>Mixed corpus. The complete corpus cannot be publicly distributed due <br>to copyright restrictions on a subset of the source materials.</p> |
| title | Automatically detecting codeswitching between Nigerian Pidgin English and English |
| url | https://doi.org/10.5281/zenodo.20117988 |