POS-tagging to highlight the skeletal structure of sentences
This work addresses sentence structure extraction for Russian language processing, but it is incremental as it applies an existing method to a new dataset.
The study developed a part-of-speech tagging model using BERT transfer learning to extract skeletal sentence structures from Russian text, demonstrating effectiveness with potential applications in NLP tasks like machine translation.
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.