Abstract:
Imprecise diagrams (those with malformed, missing, or extraneous features) occur in many situations. We propose a five-stage architecture for interpreting such diagrams and have implemented a tool, within this architecture, for automatically grading answers to examination questions. The approach is based on identifying (possibly malformed) minimal meaningful units and interpreting them to yield a meaningful result. Early indications are that the tool’s performance is similar to that of human markers.
Artificial intelligence |
Machine learning |
Natural language processing |
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