This paper explores the lemmatization of multi-word expressions (MWEs) and proper names in Polish -tasks complicated by linguistic irregularities and historical factors.Instead of using rule-based methods, we apply a machine learning approach with fine-tuned plT5 and mT5 models.We trained and validated the models on enhanced gold-standard data from the 2019 Pol-Eval task and evaluated the impact of additional fine-tuning on a silver-standard dataset derived from Wikipedia.Two setups were tested: one without context, and one using leftside context of the target MWE.Our best model achieved 86.23% AccCS (Accuracy Case-Sensitive), 89.43% AccCI (Accuracy Case-Insensitive), and a combined score of 88.79%, setting a new state-of-the-art for Polish MWE and named entity lemmatization, as confirmed by the PolEval maintainers.We also evaluated optimization and quantization techniques to reduce model size and inference time with modest quality loss.

The article discusses the problem of automatic detection of amendments found in the Polish statutory law. We treat the problem as a token-classification task and we introduce a scheme constructed by analysis of more than 200 amending bills. We apply recent neural architectures such as BERT and BiRNN to the task of token classification. The achieved results of all models are very high as micro average F1 score ranges from 96.3% to 98.2% for BiRNN. The presented solution is a first step towards fully automatic structuring and application of amendments in the Polish statutory law.