Data annotation and logical verification expert
Scope & Objective: Assisted in training a specialized NLP model by providing high-quality text annotation and semantic labeling for a dataset focused on conversational AI / logical reasoning. Specific Tasks: Performed Named Entity Recognition (NER), text categorization, and logic-based evaluation of model outputs. Audited machine-generated text for grammatical accuracy, factual consistency, and policy violations. Project Size: Successfully processed and labeled 3,000+ text samples within a 3-month timeframe. Quality Measures: Adhered to a multi-pass review pipeline, achieving an internal quality audit score of 98. Utilized spot-checking and consensus routing to resolve ambiguous linguistic edge cases.