GRDD: A Dataset for Greek Dialectal NLP
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arXiv
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| Autori principali: | , , , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2023
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| _version_ | 1866918160340353024 |
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| author | Chatzikyriakidis, Stergios Qwaider, Chatrine Kolokousis, Ilias Koula, Christina Papadakis, Dimitris Sakellariou, Efthymia |
| author_facet | Chatzikyriakidis, Stergios Qwaider, Chatrine Kolokousis, Ilias Koula, Christina Papadakis, Dimitris Sakellariou, Efthymia |
| contents | In this paper, we present a dataset for the computational study of a number of Modern Greek dialects. It consists of raw text data from four dialects of Modern Greek, Cretan, Pontic, Northern Greek and Cypriot Greek. The dataset is of considerable size, albeit imbalanced, and presents the first attempt to create large scale dialectal resources of this type for Modern Greek dialects. We then use the dataset to perform dialect idefntification. We experiment with traditional ML algorithms, as well as simple DL architectures. The results show very good performance on the task, potentially revealing that the dialects in question have distinct enough characteristics allowing even simple ML models to perform well on the task. Error analysis is performed for the top performing algorithms showing that in a number of cases the errors are due to insufficient dataset cleaning. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2308_00802 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | GRDD: A Dataset for Greek Dialectal NLP Chatzikyriakidis, Stergios Qwaider, Chatrine Kolokousis, Ilias Koula, Christina Papadakis, Dimitris Sakellariou, Efthymia Computation and Language In this paper, we present a dataset for the computational study of a number of Modern Greek dialects. It consists of raw text data from four dialects of Modern Greek, Cretan, Pontic, Northern Greek and Cypriot Greek. The dataset is of considerable size, albeit imbalanced, and presents the first attempt to create large scale dialectal resources of this type for Modern Greek dialects. We then use the dataset to perform dialect idefntification. We experiment with traditional ML algorithms, as well as simple DL architectures. The results show very good performance on the task, potentially revealing that the dialects in question have distinct enough characteristics allowing even simple ML models to perform well on the task. Error analysis is performed for the top performing algorithms showing that in a number of cases the errors are due to insufficient dataset cleaning. |
| title | GRDD: A Dataset for Greek Dialectal NLP |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2308.00802 |