INAGQA: Headword Extraction Accuracy for German Financial Question Answering

INAGQA, a novel QA system, achieved 0.91 F1 score on German financial queries using a hybrid disambiguation algorithm. The dataset likely contains accuracy metrics for headword-centric parsing, validated on 2,100 expert-annotated questions. Jamal Al Qundus published this 5.5 KB Excel file on figshare in May 2026.

Use Cases

• Benchmark headword extraction algorithms based on the reported 0.91 F1 score for German financial queries.
• Evaluate domain-optimized shallow parsing techniques based on the described 35% reduction in relation-linking errors for compound nouns.
• Compare QA system performance against BERT-KGQA and template-based systems using the provided F1 score comparisons.
• Analyze system response times for financial applications based on the 2.1s average reported in a case study.

Strengths

• Validation on 2,100 expert-annotated questions provides a substantial basis for evaluation.
• Reports specific performance metrics, including a 0.91 F1 score and a 35% error reduction for compound nouns.
• Includes comparative benchmarks against BERT-KGQA (F1: 0.83) and template-based systems (F1: 0.79).

Limitations

• Row count is unknown, which may limit suitability assessment.
• Column-level documentation is absent; field semantics must be inferred after download.
• The dataset is very small at 5.5 KB, indicating limited scope.

Provenance

Source

figshare, authored by Jamal Al Qundus.

Collection Method

Likely derived from experiments evaluating the INAGQA question-answering system.

Freshness

Last updated 2026-05-04 17:51:20; freshness should be verified.