Build and evaluate text classification, extraction, retrieval, and drafting workflows. Explore sentiment analysis and evidence-based grant outlines with explici
Last reviewed: 2026-10-03
Natural language processing includes text classification, extraction, retrieval, and generation. Sentiment analysis is a classification task with labels defined for a particular dataset and context. Evaluate language, negation, and ambiguous examples instead of treating a positive or negative score as a person's complete intent.
For trend analysis, aggregate permitted text with a documented sample and time window. For a proposed grant-writing exercise, extract requirements from a funder's published call and draft an outline using verified project facts. A human should check eligibility, evidence, citations, and final wording; fluent text is not proof that a requirement is met.
Yes. Task formulation, labeled evaluation, language-specific error analysis, and extraction validation apply whether an implementation uses rules, a trained classifier, or a prompted language model.
Show which requirement and source passage supports each part of the draft, identify missing project evidence, and retain human review. Do not invent outcomes, budgets, eligibility, or funder commitments.