GDN-CC: Manually Annotated Corpus for Democratic Consultation Clarification

GDN-CC is a manually annotated dataset for the Corpus Clarification task, introduced by Lequeu et al. in 2026. The dataset transforms noisy, multi-topic citizen contributions from large-scale consultations into structured argumentative units. The dataset was uploaded by LequeuISIR to Hugging Face and last updated on April 28, -2026.

Use Cases

• Preprocessing noisy citizen contributions for analysis based on the described transformation framework.
• Training models for argument unit segmentation and structuring based on the corpus clarification task.
• Benchmarking NLP systems on political consultation text based on the manual annotations mentioned.

Strengths

• Manually annotated, which suggests a degree of quality control.
• Designed for a specific, defined preprocessing task (Corpus Clarification).
• Introduced in a referenced academic paper (Lequeu et al. 2026).

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Description metadata is limited; actual data quality requires manual inspection after download.

Provenance

Source

LequeuISIR on Hugging Face, associated with the paper 'The GDN-CC Dataset: Automatic Corpus Clarification for AI-enhanced Democratic Citizen Consultations'.

Collection Method

Manually annotated; likely derived from contributions to the Grand Débat National.

Time Range

null

Freshness

Last updated 2026-04-28 14:49:06; freshness should be verified.

Geography

Likely France, based on the 'Grand Débat National' reference, but not explicitly confirmed.