arXiv:2602. 17663v3 Announce Type: replace Abstract: HIPE-2026 is a CLEF evaluation lab dedicated to person-place relation extraction from noisy, multilingual historical texts.
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Was this person ever at that place, and if so, when? Answering such questions from noisy, multilingual historical documents is the central challenge of HIPE-2026, the third edition of the HIPE evaluation series.
The paper introduces CLAW-4L, a benchmark of 300 pairs of English and non‑English Wikipedia biographies (French, Chinese, Azerbaijani) focused on women from non‑English contexts, complete with claim annotations and a fine‑grained claim‑pair relation corpus. It proposes a claim‑based enrichment framework that extracts claims from both biographies, aligns them to identify enrichment evidence from the non‑English version, and rewrites the English biography accordingly. Experiments demonstrate that non‑English Wikipedia biographies can improve English biography coverage, though lower‑resource settings still pose challenges.
The FAIR Digital Object (FDO) framework mandates that metadata attribute values be expressed as persistent identifiers (PIDs) wherever possible, to produce a fully machine-actionable graph in which ev...
arXiv:2609.00832v1 Announce Type: new Abstract: The exponential growth of scientific publications calls for automatic Information Extraction (IE) systems to support knowledge discovery. In this conte...
arXiv:2605. 06142v2 Announce Type: replace-cross Abstract: When people recount personal memories, they often refer to people, places, and events indirectly, relying on con-textual cues rather than explicit names.
PiPMRE is a new pipeline for medical relation extraction that uses language models instead of traditional tagging schemes. The framework includes a relation generator that produces multiple relational triplets from a text and a relation filter that scores and selects the most reliable triplets. Experiments on two public datasets show that PiPMRE outperforms previous state‑of‑the‑art methods, improving recall by 5.6 points and accuracy by 4.4 points, and it also performs well in few‑shot scenarios.