Papers › POS-tagging to highlight the skeletal structure of sentences

POS-tagging to highlight the skeletal structure of sentences

21 Nov 2024arXiv:2411.14393archive 2025-07-28

Grigorii Churakov

This study presents the development of a part-of-speech (POS) tagging model to extract the skeletal structure of sentences using transfer learning with the BERT architecture for token classification. The model, fine-tuned on Russian text, demonstrating its effectiveness. The approach offers potential applications in enhancing natural language processing tasks, such as improving machine translation. Keywords: part of speech tagging, morphological analysis, natural language processing, BERT.

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Code

disk0Dancer/rubert-finetuned-pos officialmentioned on GitHub report

huggingface.co/disk0dancer/ruBert-base-finetuned-pos official report

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Tasks

Machine Translation Morphological Analysis POS POS Tagging Part-Of-Speech Tagging Token Classification Transfer Learning Translation token-classification

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Russian Sentences POS tagged

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Methods

Adam Attention Attention Dropout BERT Dense Connections Dropout Layer Normalization Linear Layer Linear Warmup With Linear Decay Multi-Head Attention Residual Connection Softmax Weight Decay WordPiece

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