cv-structure-analysis

You identify and classify CV sections and headers by applying rule-based parsing logic to structured or semi-structured CVs, enabling deterministic downstream analysis and reproducible data extraction. This approach stabilizes section recognition across diverse formats.

Yes, CV section identification supports German and English coverage. The rule-based parsing logic includes extensibility hooks that allow you to apply and improve language-specific section detection across diverse CV formats.

Yes, you can extend the section logic using clear extensibility hooks provided in the core analyzer module. This allows you to expand parsing rules and integrate with existing CV analysis pipelines while maintaining robustness through unit tests.

The best way to ensure stable header recognition is using a deterministic, rule-based parsing approach rather than probabilistic methods. This provides transparent section identification and ensures reproducible data extraction from varied CV structures.

Inconsistent section boundaries often occur when parsing lacks deterministic rules for header detection. Applying a strict, rule-based structure analysis stabilizes section recognition and ensures reproducible data extraction across diverse and semi-structured CV formats.