Parallel Bidirectionally Pretrained Taggers as Feature Generators

Applied Sciences

Ranka Stanković¹  Mihailo Škorić¹  Branislava Šandrih Todorović² 

[1]Faculty of Mining and Geology, University of Belgrade, Djusina 7, 11120 Belgrade, Serbia

[2]Faculty of Philology, University of Belgrade, Studentski Trg 3, 11000 Belgrade, Serbia

关键词: annotation;    natural language processing;    feature extraction;    composite structures;    part of speech;   

DOI  :  10.3390/app12105028

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown