Clinical nursing documentation contains detailed observational information that is essential for patient monitoring and clinical decision-making, yet this information is predominantly recorded in free-text form. The MEDIQA-SYNUR shared task addresses this challenge by requiring systems to extract structured nursing observations from clinical transcripts under strict constraints on evidence grounding and value normalization. In this work, we present a multi-agent large language model (LLM)–based system for the MEDIQA-SYNUR task. We utilize the Llama3 open source LLM for this purpose for ease of local deployment within hospital digital infrastructure. Our system decomposes the extraction process into specialized agents responsible for schema-guided extraction, rule-based validation, and precision-oriented filtering. Starting from a baseline multi-agent pipeline, we conduct a systematic error analysis over the entire development set, examining all false positive and false negative predictions. Our final configuration, selected after extensive exploration and error analysis, combined transcript segmentation, the precision agent, and a suppression table derived from development-set analysis. On the development set, this setup achieved an F1 score of 0.6930 (precision = 0.6427, recall = 0.7518). Applying the same configuration directly to the test set, without any additional tuning, yielded an F1 score of 0.5923 (precision = 0.5292, recall = 0.6725). These results represent the most effective balance of precision and recall achieved through our iterative refinements and reflect the final state of the system as submitted for the competition

In this paper, I present an approach using one-vs-one classification scheme with TF-IDF term weighting on character n-grams for identifying Arabic dialects used in social media. The scheme was evaluated in the context of the third Nuanced Arabic Dialect Identification (NADI 2022) shared task for identifying Arabic dialects used in Twitter messages. The approach was implemented with logistic regression loss and trained using stochastic gradient decent (SGD) algorithm. This simple method achieved a macro F1 score of 22.89% and 10.83% on TEST A and TEST B, respectively, in comparison to an approach based on AraBERT pretrained transformer model which achieved a macro F1 score of 30.01% and 14.84%, respectively. My submission based on AraBERT scored a macro F1 average of 22.42% and was ranked 10 out of the 19 teams who participated in the task.