We present a hybrid extraction system that combines large language model capabilities with rule-based precision for extracting structured clinical observations from nursing dictation transcripts. Our approach leverages Claude Opus 4.5 as the primary extractor, enhanced with comprehensive prompt engineering that includes the complete 193-concept schema, few-shot examples, and detailed validation rules covering respiratory, cardiac, diagnosis, and mental status fields. The LLM output undergoes extensive post-processing with six specialized filters that remove speculative diagnoses, validate physiological ranges, ensure unit-field dependencies, and verify contextual appropriateness. Five correction mechanisms normalize breathing patterns, map dyspnea severity, standardize assistance levels, clean STRING fields, and handle multi-select conjunctions. A supplementary rule-based component employs 400+ regex patterns with contextual validation to capture high-confidence observations, particularly for vital signs and categorical fields. The system requires cardiac keywords for heart rate extraction and respiratory context for respiration rates, preventing false positives from unrelated numeric values. Results are merged through an intelligent strategy that prioritizes LLM comprehensiveness while supplementing with rule-based findings. A strict schema validation layer ensures all four value types (NUMERIC, STRING, SINGLE_SELECT, MULTI_SELECT) conform to enumerated options and physiological ranges. This multi-layered approach balances recall through LLM reasoning with precision through rule-based validation, effectively structuring natural nursing narratives into standardized EHR-ready observations.

This paper describes our proposed system & experiments performed to detect contextual emotion in texts for SemEval 2019 Task 3. We exploit sentiment information, syntactic patterns & semantic relatedness to capture diverse aspects of the text. Word level embeddings such as Glove, FastText, Emoji along with sentence level embeddings like Skip-Thought, DeepMoji & Unsupervised Sentiment Neuron were used as input features to our architecture. We democratize the learning using ensembling of models with different parameters to produce the final output. This paper discusses comparative analysis of the significance of these embeddings and our approach for the task.