Citation identification in historical and ancient texts poses challenges that extend beyond surface-level pattern recognition, including implicit references, morphological fusion, and discourse-driven ambiguity. In this work, we address citation Named Entity Recognition (NER) in medieval Hebrew Responsa literature using a modular, LLM-based correction pipeline. Rather than treating large language models as end-to-end predictors, we leverage them as structured components: an initial prompt-based expert tagger, complementary LLM judges for systematic error detection, and domain-aware correction grounded in philological regularities. Our approach requires no end-to-end fine-tuning and only minimal labeled supervision (a small validation set for training a lightweight error-detection classifier), narrowing the performance gap to strong supervised models trained on domain-specific data. The results suggest that explicit error handling and interpretability-driven design offer a promising direction for historical NLP in low-resource settings.

In this paper, we provide the first quantified exploration of the structure of the language of dreams, their linguistic style and emotional content. We present a collection of digital dream logs as a viable corpus for the growing study of mental health through the lens of language, complementary to the work done examining more traditional social media. This paper is largely exploratory in nature to lay the groundwork for subsequent research in mental health, rather than optimizing a particular text classification task.