评测多语言历史文本中人物地点关系抽取的准确与效率。
CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts
- 区分'曾到过'和'当时在场'两种语义关系，需结合时空线索推理。
- 评估系统在准确性、计算效率与跨领域泛化上的综合表现。
- 适合数字人文、知识图谱构建等历史数据研究者使用。
HIPE-2026 是 CLEF 举办的评估实验室，专注于从嘈杂的多语言历史文本中提取人物-地点关系。继 HIPE-2020 与 HIPE-2022 之后，该活动将任务扩展至语义关系抽取，聚焦于识别多语言、多时期文本中的人物-地点关联。系统需分类两类关系：$at$（“此人是否曾到过此地？”）与 $isAt$（“此人是否在出版时间附近位于此处？”），要求结合时间与地理线索进行推理。评测采用三重指标体系，联合评估准确性、计算效率与领域泛化能力。通过连接关系抽取与大规模历史数据处理，HIPE-2026 致力于支持知识图谱构建、历史人物传记重建及数字人文中的空间分析等下游应用。
原文摘要 · Abstract (English)
HIPE-2026 is a CLEF evaluation lab dedicated to person-place relation extraction from noisy, multilingual historical texts. Building on the HIPE-2020 and HIPE-2022 campaigns, it extends the series toward semantic relation extraction by targeting the task of identifying person-place associations in multiple languages and time periods. Systems are asked to classify relations of two types -- $at$ ("Has the person ever been at this place?") and $isAt$ ("Is the person located at this place around publication time?") -- requiring reasoning over temporal and geographical cues. The lab introduces a three-fold evaluation profile that jointly assesses accuracy, computational efficiency, and domain generalization. By linking relation extraction to large-scale historical data processing, HIPE-2026 aims to support downstream applications in knowledge-graph construction, historical biography reconstruction, and spatial analysis in digital humanities.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。