Parallel Bidirectionally Pretrained Taggers as Feature Generators
Објеката
- Тип
- Рад у часопису
- Верзија рада
- објављена верзија
- Језик
- енглески
- Креатор
- Ranka Stanković, Mihailo Škorić, Branislava Šandrih Todorović
- Извор
- Applied Sciences
- Издавач
- MDPI AG
- Датум издавања
- 2022
- Сажетак
- In a setting where multiple automatic annotation approaches coexist and advance separately but none completely solve a specific problem, the key might be in their combination and integration. This paper outlines a scalable architecture for Part-of-Speech tagging using multiple standalone annotation systems as feature generators for a stacked classifier. It also explores automatic resource expansion via dataset augmentation and bidirectional training in order to increase the number of taggers and to maximize the impact of the composite system, which is especially viable for low-resource languages. We demonstrate the approach on a preannotated dataset for Serbian using nested cross-validation to test and compare standalone and composite taggers. Based on the results, we conclude that given a limited training dataset, there is a payoff from cutting a percentage of the initial training set and using it to fine-tune a machine-learning-based stacked classifier, especially if it is trained bidirectionally. Moreover, we found a measurable impact on the usage of multiple tagsets to scale-up the architecture further through transfer learning methods.
- том
- 12
- издање
- 10
- doi
- 10.3390/app12105028
- issn
- 2076-3417
- Subject
- анотација, обрада природног језика, издвајање обележја, композитне структуре, врста речи
- annotation, natural language processing, feature extraction, composite structures, part of speech
- Шира категорија рада
- M20
- Ужа категорија рада
- М22
- Права
- Отворени приступ
- Лиценца
- All rights reserved
- Формат
- Медија
applsci-12-05028.pdf
Ranka Stanković, Mihailo Škorić, Branislava Šandrih Todorović. "Parallel Bidirectionally Pretrained Taggers as Feature Generators" in Applied Sciences, MDPI AG (2022). https://doi.org/10.3390/app12105028
This item was submitted on 16. мај 2022. by [anonymous user] using the form “Рад у часопису” on the site “Радови”: http://gabp-dl.rgf.rs/s/repo
Click here to view the collected data.