AniEE: A Dataset of Animal Experimental Literature for Event Extraction (2023.findings-emnlp)
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| Challenge: | Event extraction (EE) is a crucial information extraction task in biomedical domain . existing biomedically EE datasets focus on cell experiments or overall procedures . |
| Approach: | They propose an animal experiment customized entity and event scheme for event extraction . they create an expert-annotated high-quality dataset containing discontinuous entities and nested events . |
| Outcome: | The proposed dataset is based on the animal experiment stage and a NER and EE model. |
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MEE: A Novel Multilingual Event Extraction Dataset (2022.emnlp-main)
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| Challenge: | Existing methods for Event Extraction are limited for non-English languages . lack of high-quality multilingual datasets has been the main hindrance . |
| Approach: | They propose a multilingual event extraction dataset that provides annotation for more than 50K event mentions in 8 typologically different languages. |
| Outcome: | The proposed dataset provides annotation for more than 50K event mentions in 8 languages . the proposed dataset will be publicly available to foster future research . |
BKEE: Pioneering Event Extraction in the Vietnamese Language (2024.lrec-main)
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| Challenge: | Event Extraction (EE) is a fundamental task in information extraction. |
| Approach: | They propose a Vietnamese event extraction dataset that includes 33 different event types and 28 different event argument roles. |
| Outcome: | The proposed dataset provides a labeled dataset for entity mentions, event mentions and event arguments on 1066 documents. |
Evaluating Zero-Shot Event Structures: Recommendations for Automatic Content Extraction (ACE) Annotations (2023.acl-short)
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| Challenge: | Zero-shot event extraction (EE) methods infer richly structured event records from unstructured text data, based on a user-supplied natural language specification and no training examples. |
| Approach: | They propose recommendations for future evaluations so the research community can better utilize ACE as an event evaluation resource. |
| Outcome: | The proposed methods can be used to evaluate zero-shot and other low-supervision EE methods, considering up to 32% of correctly identified arguments and 25% of correctly ignored event mentions as false negatives. |
The Devil is in the Details: On the Pitfalls of Event Extraction Evaluation (2023.findings-acl)
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| Challenge: | Event extraction (EE) is a fundamental information extraction task aimed at extracting events from plain texts. |
| Approach: | They propose to specify data preprocessing, standardize outputs, and provide pipeline evaluation results to avoid these pitfalls. |
| Outcome: | The results show that the evaluations are reliable and lack pipeline evaluations. |
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 . |
| Approach: | They propose a large-scale document-level event information extraction dataset with over 56,000+ events and 242,000+ arguments. |
| Outcome: | The proposed dataset has over 56,000+ events and 242,000+ arguments. |
Event Extraction from Historical Texts: A New Dataset for Black Rebellions (2021.findings-acl)
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| Challenge: | Using EE to extract historical data, we study the discourse of slave and non-slave African diaspora rebellions published in the periodical press in this period. |
| Approach: | They propose a dataset to detect event trigger words and their arguments in nineteenth-century newspapers. |
| Outcome: | The proposed dataset features 5 entity types, 12 event types, and 6 argument roles that concern slavery and black movements between the eighteenth and nineteenth centuries. |
SciEvent: Benchmarking Multi-domain Scientific Event Extraction (2025.emnlp-main)
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| Challenge: | Existing work on scientific information extraction relies on entity-relation extraction in narrow domains . current models struggle in domains such as sociology and humanities . |
| Approach: | They propose a multi-domain benchmark for scientific abstract annotations using a unified event extraction schema. |
| Outcome: | The proposed benchmark includes 500 abstracts across five research domains with manual annotations of event segments, triggers, and fine-grained arguments. |
Multilingual SubEvent Relation Extraction: A Novel Dataset and Structure Induction Method (2022.findings-emnlp)
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| Challenge: | Existing methods for subevent relation extraction (SRE) focus on sequential order of words in texts to enhance representation learning. |
| Approach: | They propose a method that learns to induce effective graph structures for input texts . they use word alignment frameworks with dependency paths and optimal transport . |
| Outcome: | The proposed method is able to induce effective graph structures for input texts to boost representation learning. |
Adaptive Schema-aware Event Extraction with Retrieval-Augmented Generation (2025.findings-emnlp)
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| Challenge: | Event extraction is a task in natural language processing that involves identifying and extracting event information from unstructured text. |
| Approach: | They propose a paradigm that combines schema paraphrasing with schema retrieval-augmented generation. |
| Outcome: | The proposed paradigm retrieves paraphrased schemas and accurately generates targeted structures. |
Event Extraction in Video Transcripts (2022.coling-1)
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| Challenge: | Existing EE datasets are limited to formally written documents such as news articles or scientific papers . existing EE methods and datasets cannot be used in informal and noisy texts . |
| Approach: | They propose to use video transcripts as a dataset for event extraction . they demonstrate that existing state-of-the-art EE methods cannot achieve adequate performance . |
| Outcome: | The proposed dataset evaluates state-of-the-art EE methods on streamed videos on Behance . it shows that such systems cannot achieve adequate performance on the proposed dataset . |