Natural Language Processing in Grant Reviews

NLP helps reviewers by summarising long responses, extracting evidence and mapping content to criteria.

• Reduces cognitive load and speeds up assessments.
• Helps surface strengths and risks consistently.
• Keeps original text available for verification.

Useful NLP techniques

Pick simple, proven methods before complex ones.

• Abstractive summaries for long narratives.
• Keyword and entity extraction for themes and locations.
• Similarity checks against eligibility wording.

Key takeaway: practical NLP improves quality and speed without black boxes.

Reviewer experience

NLP should support, not distract, from judgement.

• Side‑by‑side views of source and summary.
• One‑click evidence capture into notes.
• Clear indicators of confidence and gaps.

Key takeaway: Plinth integrates NLP directly into the review flow.

Data quality considerations

Garbage in, garbage out still applies.

• Use plain‑English forms and constrained fields where possible.
• Encourage applicants to provide specific examples.
• Maintain an outcome library for consistent tagging.

Key takeaway: structure improves both manual and AI‑assisted reviews.

FAQs

Does NLP replace reading the application?

No. It highlights key points so reviewers can focus attention.

Are outputs explainable?

Yes. Show source snippets and maintain change logs.

What about accessibility?

Summaries can be read aloud or translated to support inclusive panels.