[Submitted on 5 Jul 2025 (v1), last revised 17 Oct 2026 (this version, v2)]
Abstract:Person descriptions in public-safety text can be compact and regular, or distributed across heterogeneous narratives in which evidence boundaries and person ownership are ambiguous. We present POSID, a target-task-unsupervised framework that decomposes person-attribute extraction into candidate localization, provenance-controlled extraction scope, structured recovery and traversal, and person association, without supervised training or fine-tuning on the target task. We evaluate six localization configurations under two traversal policies across three real-use cohorts and two controlled synthetic diagnostics. On structurally regular descriptions, the strongest configurations reach 1.000/.972 Attr-Only (AO)/Attr-Value (AV) F1 on Compact500. Across the real-use cohorts, every RE-containing configuration benefits from conservative early stopping, while semantic-only retrieval can benefit from continued traversal when localization is less constrained. On the heterogeneous multi-person Challenge14 cohort, the best automatic AO and AV results are .500 and .382; replacing automatic candidates with verified evidence raises the frozen extractor to .522/.410 under early stopping, showing that localization is one of the material bottlenecks. We further introduce provenance-aware scoring for collective and underspecified evidence and a separate person-aware diagnostic for ownership and fragmentation. Together, the results establish a measurable operating regime for inspectable person-attribute extraction and distinguish errors due to localization, traversal, structured recovery, and attribution.
Code: this https URL.
Submission history
From: Kma Solaiman [view email]
[v1] Sat, 5 Jul 2025 08:22:52 UTC (19 KB)
[v2] Sat, 17 Oct 2026 03:29:58 UTC (1,780 KB)
