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 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
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.
By Yan Yu, Mengna Zhu, Zhenyu Song, Hao Yang, Haiwen Chen, Mao Wang
EAGER is a reinforcement learning framework for generative event extraction that uses fine‑grained verifiable rewards and Schema‑Contrastive Advantage Estimation to address advantage collapse in sparse binary reward settings. The reward design focuses on structural validity, extraction accuracy, groundedness, coverage, over‑generation, and span precision. Across seven benchmark datasets, EAGER outperforms prompting, supervised fine‑tuning, and previous reinforcement learning baselines, achieving a substantial improvement over the strongest prior method.
By Omar Adjali, Siting Liang, Omair Shahzad Bhatti, Daniel Sonntag
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.
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
arXiv:2608. 08636v1 Announce Type: cross Abstract: Scientific named entity recognition (SciNER) plays a crucial role in information extraction and knowledge discovery from scientific texts.
By Tong Bao, Yi Zhao, Heng Zhang, Chengzhi Zhang
The paper introduces ThinkTwice, a framework that uses sampling and selection to improve document‑level information extraction. Instead of relying on greedy decoding, the method generates multiple candidate templates and selects the best one via an unsupervised agreement‑based approach or a supervised reward model. It also proposes a rejection‑sampling technique to create silver training data for reasoning traces, and experimental results show that ThinkTwice consistently outperforms greedy baselines and the current supervised state‑of‑the‑art.
By Mikel Zubillaga, Oscar Sainz, Oier Lopez de Lacalle, Eneko Agirre
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)
arXiv:2606. 15412v1 Announce Type: cross Abstract: Biomedical relation extraction (BioRE) is a key step in transforming biomedical literature into structured knowledge.
By Jakob Mraz, Toma\v{z} Curk, Bla\v{z} Zupan
arXiv:2608.24268v2 Announce Type: replace
Abstract: This paper proposes methods to extract over 50 types of events from a Dutch historical corpus spanning the 17th and 18th centuries. The methods we...
By Stella Verkijk, Piek Vossen
arXiv:2610.19021v1 Announce Type: new
Abstract: Recent work has questioned whether large language models (LLMs) can be useful for temporal event prediction. Despite vast pretrained world knowledge, L...
By Zijun Wu, Zhitian Zhang, Hossein Hajimirsadeghi, Nikita Dvornik, Alexander Pashevich