SciNLP is a new benchmark dataset for full‑text entity and relation extraction in the NLP domain, comprising 60 manually annotated papers with 6,429 entities and 1,649 relations. It is the first dataset to provide full‑text annotations of entities and their relationships specifically for NLP literature. Experiments show that models trained on SciNLP outperform baselines on certain tasks, and the dataset enabled the automatic construction of a fine‑grained knowledge graph with an average node degree of 3.3.
By Decheng Duan, Yingyi Zhang, Jitong Peng, Chengzhi Zhang
The paper introduces a weakly supervised framework for extracting dataset mentions from forced displacement and Fragile, Conflict, and Violence (FCV) documents. It uses a lightweight model trained on general research literature to generate candidate mentions, which are then refined by a large language model that validates or rejects them and corrects boundaries. The refined annotations are augmented with synthetic and contrastive examples to fine‑tune the model, achieving 74.1% precision and 70.5% recall on a benchmark of 1,706 passages, with higher precision (89.5%) on passages that contain dataset references.
By Rafael Macalaba, Aivin V. Solatorio, Patrick Michael Brock, Olivier Dupriez
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.
CHisAgent is a multi‑agent large language model framework designed to build event taxonomies for ancient Chinese cultural contexts. It splits the task into three specialized stages: an Inducer that creates an initial hierarchy from raw historical texts, an Expander that adds missing intermediate concepts using LLM world knowledge, and an Enricher that incorporates external structured historical resources to ensure faithfulness. Applied to the Twenty‑Four Histories, CHisAgent produced a comprehensive, domain‑aware taxonomy covering politics, military, diplomacy, and social life, and evaluations show improved structural coherence, coverage, and cross‑cultural alignment.
By Xuemei Tang, Chengxi Yan, Jinghang Gu, Chu-Ren Huang
arXiv:2607. 21610v1 Announce Type: cross Abstract: Schema graphs are an upstream bottleneck of schema-grounded information extraction and knowledge graph construction, yet most extraction systems assume the schema is already available.
By Miaobo Hu, Xiaobo Guo, Shuhao Hu, Bokun Wang, Rui Chen, Xin Wang, Daren Zha, Jun Xiao
arXiv:2510. 06039v2 Announce Type: replace-cross Abstract: Reliable evaluation of knowledge-grounded Large Language Models (LLMs) in Chinese requires resources that explicitly align Chinese-language text with verifiable Knowledge Graph (KG) facts.
By Chengwei Wu, Xingrui Zhuo, Mingyang Gao, Xinghe Cheng, Zhichao Yan, Jiapu Wang
arXiv:2606. 19710v1 Announce Type: cross Abstract: Court proceedings contain valuable evidence about human smuggling networks, but this information is often buried within unstructured, jargon-heavy legal documents.
By Elijah Feldman, Dipak Meher, Carlotta Domeniconi
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 a configurable semantic chunking framework for biomedical information extraction in retrieval‑augmented generation systems. It replaces the fixed‑size chunking stage of BioMedRAG with entity‑preserving windows, trigger‑centered chunking, proposition‑first extraction, tiered trigger prioritization, and hierarchical relation resolution, while keeping the rest of the pipeline unchanged. Experiments on relation extraction benchmarks (GM‑CIHT, DDI, ChemProt) and adverse event classification (ADE) show that the hybrid configuration boosts performance on datasets with explicit relation cues, achieving 82.6% F1 on GM‑CIHT compared to 74.2% with the baseline.
By Riya Ahuja (Institute of Data Science in Biomedicine, TU Braunschweig, Braunschweig, Germany, Braunschweig Integrated Centre of Systems Biology, TU Braunschweig, Braunschweig, Germany), Tim Kacprowski (Institute of Data Science in Biomedicine, TU Braunschweig, Braunschweig, Germany, Braunschweig Integrated Centre of Systems Biology, TU Braunschweig, Braunschweig, Germany), Roya Shiasi Sardoabi (Institute of Data Science in Biomedicine, TU Braunschweig, Braunschweig, Germany, Braunschweig Integrated Centre of Systems Biology, TU Braunschweig, Braunschweig, Germany)
The paper introduces a scalable cross‑domain event extraction system built on a unified generative sequence‑to‑sequence framework. It jointly handles event detection and argument extraction, allowing both pipeline and end‑to‑end configurations. By fine‑tuning pretrained language models on multiple event datasets from diverse domains, the system retains domain‑specific semantics while generalizing across large, evolving label spaces, and offers a web‑based application for researchers to upload documents, extract events, visualize triggers and arguments, and compare configurations.
By Siting Liang, Omar Adjali, Omair Shahzad Bhatti, Daniel Sonntag
The paper introduces a unified multi-domain and multi-task generative framework for event extraction that incorporates explicit domain conditioning signals and task-specific prompts. This design allows a single model to adapt dynamically to different event schemas without needing full event label sets during inference, supporting both pipeline and end-to-end extraction. Experiments on various benchmarks show competitive performance, strong cross-domain generalization, and practical scalability while maintaining domain-specific precision.
By Siting Liang, Omar Adjali, Daniel Sonntag
The paper introduces MACE, a Multi-Agent Candidate Event acquisition method designed to improve event linking by refining event structure before the linking step. MACE employs evidence-specialized large language model agents to gather time, location, participant, and event-type evidence, exposes intermediate queries to candidate-event lookup tools, and allows a coordinator to revise the evidence set before final candidate construction. Experiments on two event linking benchmarks demonstrate that integrating MACE consistently boosts accuracy across different event linking models without altering the underlying models.
By Ziyang Zhang, Yinan Liu, Boyi Xue, Yingxuan Huang, Bin Wang, Xiaochun Yang