AI Problem-Method Recognition (PMRC)
PMRC recognition extracts structured research logic from papers: problem, method, result, and contribution. It is different from topic labeling because it focuses on what problem a paper solves, how it solves it, what result it obtains, and what contribution it claims.
When To Use It
- Build a problem-method matrix for a research field.
- Discover common methods used to solve a class of problems.
- Identify gaps between research questions and available methods.
- Prepare structured evidence for review, consultation, or subject service reports.
Open The Window
Use one of the following entries:
AI -> AI Problem-Method Recognition- Right-click selected papers in the document table and open the PMRC function.
The window includes document status, P results, M results, R results, C results, PMRC results, and PMRC analysis tabs.
Configure Recognition
Right-click the document table and choose Start Recognition. Configure the model, API key, batching, evidence length, maximum PMRC groups per paper, and prompt language. Chinese environments use Chinese prompts; other environments use English prompts.
For stable output, require specific problems and explicit methods. Avoid broad labels such as generic experiment, empirical analysis, or method study unless the abstract contains concrete evidence.
Review And Re-run
Documents without abstracts, review articles, and papers without clear problem-method evidence may be ignored. Failed or low-quality records can be re-run after adjusting prompts, batch size, or evidence length.
Use PMRC outputs with topic and competitiveness views when you need to explain not only which direction is active, but also what technical problems and methods drive that direction.
Interface Screenshots
The screenshots below are retained to verify menu entries, parameter settings, result views, and export locations.
