CLEF HIPE-2026: Evaluating Accurate and Efficient Person-Place Relation Extraction from Multilingual Historical Texts
AI 摘要
HIPE-2026评估多语言历史文本中准确高效的Person-Place关系抽取，支持历史数据处理下游应用。
主要贡献
- 构建Person-Place关系抽取评估数据集
- 评估准确率、计算效率和领域泛化能力
- 推动知识图谱构建、历史传记重建和空间分析等应用
方法论
通过CLEF评估实验室，提供多语言历史文本，要求系统分类person-place的$at$和$isAt$关系，评估准确性和效率。
原文摘要
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.