Comparing Transformer-Based Approaches for Term Recognition in Russian texts

The paper describes an experimental comparative study of three transformer-based approaches applied for automatic term recognition (ATR) in Russian texts.The approaches include sequential labeling of word tokens in a given text, phrase classification with context sentence enclosing the phrase, and text span prediction by using vector representations obtained by contrastive learning.The BERT-based models were trained and evaluated on the data of RuTermEval-2024 competition for nested term identification and classification, which encompassed three tasks: binary term identification, term recognition with classification (into one of predefined types), and cross-domain term recognition.The experiments have shown that the span prediction models based on contrastive learning outperform the other models across all three RuTermEval tasks, but at the same time demonstrate the most significant decrease in quality in the cross-domain task.