Emotion detection is an important text classification task with applications in sentiment analysis, social media monitoring, human-computer interaction, and affective language understanding. However, Punjabi written in the Shahmukhi script remains severely under-resourced for emotion detection, with limited benchmark-style resources available for supervised evaluation. This paper introduces ShahiEmotion, a new Punjabi Shahmukhi emotion detection dataset containing 30379 sentence-level instances annotated with seven emotion categories: sadness, surprise, happiness, anger, neutral, fear, and disgust. The dataset is designed to support research in a low-resource setting characterized by script-specific challenges, lexical variation, and substantial class imbalance. We establish baseline results using several pretrained transformer-based models and formulate emotion detection as a sentence-level classification task. In particular, we fine-tune multilingual BERT, multilingual DistilBERT, XLM-RoBERTa, and Urdu RoBERTa under the same training and evaluation setting using standard cross-entropy loss. Experimental results show that XLM-RoBERTa provides the strongest overall performance among the compared models. The best model achieves 77.95% accuracy, 58.47% macro-F1, and 77.60% weighted-F1 on the test set. The dataset, evaluation protocol, and baseline results introduced in this work are intended to support future research on Punjabi Shahmukhi emotion analysis and low-resource NLP.

Lemmatization is an important preprocessing step in Natural Language Processing (NLP); however, annotated resources for medieval languages such as Old Church Slavonic (OCS) are limited in scope, size, and diversity. This paper presents the annotated resources for OCS lemmatization, including annotation process, design choices and non-standard Unicode related issues. The annotated corpus is used to evaluate existing lemmatization tools (Stanza and UDPipe-2 models trained on the UD 2.12 treebank, and a dictionary-based approach) both in cross-dataset and on a corpus obtained by merging the new annotations with existing UD V2.12 OCS data. Pretrained models perform poorly (≈ 15–16%), below a dictionary baseline (≈ 38%), while retraining on the new data improves performance (up to ≈ 51%) and shows different cross-dataset generalization. Experiments in cross-dataset and on the combined corpus demonstrate that lemmatization performance depends strongly on dataset similarity, annotation conventions, and orthographic mismatch. Overall, the findings show the value of the newly annotated resources and the importance of extending OCS lemmatization benchmarks for historical Slavic NLP.