Spanless Event Annotation for Corpus-Wide Complex Event Understanding (2024.lrec-main)
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| Challenge: | Existing methods for annotating multilingual, multimedia data are limited by the availability of multilingual corpora for schema-based event representation. |
| Approach: | They propose a new approach to event annotation to promote whole-corpus understanding of complex events in multilingual, multimedia data. |
| Outcome: | The proposed method is part of the DARPA Knowledge-directed Artificial Intelligence Reasoning Over Schemas (KAIROS) Program. |
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| Challenge: | The Schema Learning Corpus is a linguistic resource designed to support research into the structure of complex events in multilingual data. |
| Approach: | The Schema Learning Corpus is a linguistic resource that includes large volumes of background data in English, Spanish and Russian. |
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Corpus-Level Evaluation for Event QA: The IndiaPoliceEvents Corpus Covering the 2002 Gujarat Violence (2021.findings-acl)
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| Challenge: | a new corpus-level evaluation approach for event extraction is needed in social science applications . human annotations are often required to extract the actions of political actors and actors . a novel corpus evaluation approach can guide creation of similar social science-oriented resources . |
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| Challenge: | Existing approaches to multi-document event extraction have limited attention . despite its practical significance, this task has inherent challenges . |
| Approach: | They propose a collaborative framework that integrates large language models for multi-step reasoning and fine-tuned small language models to handle key subtasks. |
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Large Language Models Are Effective Human Annotation Assistants, But Not Good Independent Annotators (2026.findings-acl)
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| Challenge: | State-of-the-art NLP models are expensive and inefficient for event annotation. |
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Comprehensive Annotation of Various Types of Temporal Information on the Time Axis (L18-1)
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| Challenge: | Existing studies linking event and time information have been conducted to train and evaluate models. |
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DEIE: Benchmarking Document-level Event Information Extraction with a Large-scale Chinese News Dataset (2024.lrec-main)
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| Challenge: | Existing event-based datasets mainly target sentence-level tasks . current models struggle with "document" annotation, a key feature of the current model . |
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Event-Centric Natural Language Processing (2021.acl-tutorials)
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| Challenge: | This tutorial will provide an introduction to various methods for automating the extraction, conceptualization and prediction of events and their relations. |
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Zero-Shot On-the-Fly Event Schema Induction (2023.findings-eacl)
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| Challenge: | a new approach to event processing uses large language models to generate source documents that can be curated without manual data collection. |
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Conundrums in Event Coreference Resolution: Making Sense of the State of the Art (2021.emnlp-main)
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| Challenge: | Recent years have seen the successful application of span-based neural models to entity-based information extraction tasks such as entity coreference resolution (CR) Existing event coreference resolvers focused on feature engineering are few and far between, let alone event corefers. |
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The Causal News Corpus: Annotating Causal Relations in Event Sentences from News (2022.lrec-1)
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Fiona Anting Tan, Ali Hürriyetoğlu, Tommaso Caselli, Nelleke Oostdijk, Tadashi Nomoto, Hansi Hettiarachchi, Iqra Ameer, Onur Uca, Farhana Ferdousi Liza, Tiancheng Hu
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