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.
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 Answering such questions from noisy, multilingual historical documents is the central challenge of HIPE-2026, the third edition of the HIPE evaluation series.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.
arXiv:2606. 27651v1 Announce Type: new Abstract: In recent years, with the emergence of Temporal Knowledge Graphs (TKGs), research on learning entity and relation representations in TKGs has attracted increasing attention, giving rise to a large number of TKG embedding methods.
arXiv:2608.23263v1 Announce Type: new Abstract: The FAIR Digital Object (FDO) framework mandates that metadata attribute values be expressed as persistent identifiers (PIDs) wherever possible, to pro...
Event analysis is an essential and fundamental direction of information extraction, involving various event-centric tasks at different granularity of documents. While large language models (LLMs) have preliminarily achieved promising performance in part of these tasks individually, their capability in event analysis still lacks comprehensive understanding due to restricted document granularity, task designs, and data source of existing benchmarks.
CMNIE is a new benchmark for extracting structured information from Chinese military news, covering event triggers, arguments, named entities, and entity relations under a unified schema. The dataset contains 13,000 manually annotated instances with 7 event types, 10 argument roles, 7 entity types, and 8 relation types. Experiments show that current supervised models, zero‑shot LLMs, and fine‑tuned LLMs struggle with relation extraction and exact span matching, highlighting the challenge of joint structured extraction in this domain.
The paper introduces an ontology-driven framework to measure and enforce structural consistency in document-level relation extraction (DocRE) datasets. It identifies that many distant supervision resources, such as DocRED, contain structural noise from violations of ontology constraints and logical contradictions, which negatively affect model predictions. By incorporating structural regularization during training, the authors demonstrate a reduction in logical contradictions and improved generalization performance.
arXiv:2608.21252v1 Announce Type: cross Abstract: Question answering (QA) over long, connected documents remains challenging because relevant evidence may span multiple entities and their relationshi...