Scaling BERT Models for Turkish Automatic Punctuation and Capitalization Correction
Fuente:
arXiv
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| Autori principali: | , , , |
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| Natura: | Preprint |
| Pubblicazione: |
2024
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| _version_ | 1866917855863242752 |
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| author | Saoud, Abdulkader Alomeyr, Mahmut Kesgin, Himmet Toprak Amasyali, Mehmet Fatih |
| author_facet | Saoud, Abdulkader Alomeyr, Mahmut Kesgin, Himmet Toprak Amasyali, Mehmet Fatih |
| contents | This paper investigates the effectiveness of BERT based models for automated punctuation and capitalization corrections in Turkish texts across five distinct model sizes. The models are designated as Tiny, Mini, Small, Medium, and Base. The design and capabilities of each model are tailored to address the specific challenges of the Turkish language, with a focus on optimizing performance while minimizing computational overhead. The study presents a systematic comparison of the performance metrics precision, recall, and F1 score of each model, offering insights into their applicability in diverse operational contexts. The results demonstrate a significant improvement in text readability and accuracy as model size increases, with the Base model achieving the highest correction precision. This research provides a comprehensive guide for selecting the appropriate model size based on specific user needs and computational resources, establishing a framework for deploying these models in real-world applications to enhance the quality of written Turkish. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_02698 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Scaling BERT Models for Turkish Automatic Punctuation and Capitalization Correction Saoud, Abdulkader Alomeyr, Mahmut Kesgin, Himmet Toprak Amasyali, Mehmet Fatih Computation and Language Artificial Intelligence Machine Learning This paper investigates the effectiveness of BERT based models for automated punctuation and capitalization corrections in Turkish texts across five distinct model sizes. The models are designated as Tiny, Mini, Small, Medium, and Base. The design and capabilities of each model are tailored to address the specific challenges of the Turkish language, with a focus on optimizing performance while minimizing computational overhead. The study presents a systematic comparison of the performance metrics precision, recall, and F1 score of each model, offering insights into their applicability in diverse operational contexts. The results demonstrate a significant improvement in text readability and accuracy as model size increases, with the Base model achieving the highest correction precision. This research provides a comprehensive guide for selecting the appropriate model size based on specific user needs and computational resources, establishing a framework for deploying these models in real-world applications to enhance the quality of written Turkish. |
| title | Scaling BERT Models for Turkish Automatic Punctuation and Capitalization Correction |
| topic | Computation and Language Artificial Intelligence Machine Learning |
| url | https://arxiv.org/abs/2412.02698 |