Challenge: Using the Web, we propose a corpus for information extraction and text classification.
Approach: They propose to use a corpus for information extraction and natural language processing (NLP) tasks such as text classification.
Outcome: The proposed corpus can be used for information extraction and natural language processing tasks such as text classification.

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SPEED++: A Multilingual Event Extraction Framework for Epidemic Prediction and Preparedness (2024.emnlp-main)

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Challenge: Prior studies focused on English posts to provide early warnings for epidemic prediction, but these work focused on non-English posts.
Approach: They propose a multilingual event extraction framework for extracting epidemic event information for any disease and language using 5.1K tweets in four languages.
Outcome: The proposed framework can provide epidemic warnings for COVID-19 in its earliest stages in Dec 2019 (3 weeks before global discussions) and aggregate community epidemic discussions like symptoms and cure measures, aiding misinformation detection and public attention monitoring.
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.
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 .
Multilingual Epidemiological Text Classification: A Comparative Study (2020.coling-main)

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Challenge: a comparative study of multilingual text classification models analyzes the performance of different models based on different languages . low-resource languages are highly influenced by typology of the languages on which the models have been trained or fine-tuned but also by their size.
Approach: They compare machine and deep learning models with a dataset of epidemiological news articles . they find that the performance of the models is proportionate to the training data size .
Outcome: The proposed model outperforms baseline models on a multilingual text classification task . low-resource languages are highly influenced by typology of languages and their size .
MUSIED: A Benchmark for Event Detection from Multi-Source Heterogeneous Informal Texts (2022.emnlp-main)

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Challenge: Recent efforts to classify unstructured texts into specific types have been limited in practical scenarios.
Approach: They propose to use Chinese text conversations and phone conversations to expand event detection to the scenarios involving informal and heterogeneous texts.
Outcome: The proposed dataset is based on user reviews, text conversations, and phone conversations in a leading e-commerce platform for food service.
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 .
Approach: They propose a corpus-based approach to event extraction that integrates corpus evaluation with real-world social science . they use human annotations to read and label every document for mentions of police activity events .
Outcome: The proposed method can guide creation of similar social-science-oriented resources in the future.
PolyNarrative: A Multilingual, Multilabel, Multi-domain Dataset for Narrative Extraction from News Articles (2025.acl-long)

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Challenge: a new dataset of news articles annotated for narratives provides a framework for narrative detection . recurring narratives can propagate with very high velocity across audiences, languages and countries .
Approach: They propose a multilingual dataset annotated for narratives using two-level taxonomies . they define narrative as a recurring, repetitive, overt or implicit claim that promotes a specific interpretation or viewpoint on an ongoing topic .
Outcome: The proposed dataset will foster research in narrative detection and enable new research directions . the authors identify multiple narratives in the same article, and the results are published online .
Forecasting Future International Events: A Reliable Dataset for Text-Based Event Modeling (2024.findings-emnlp)

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Challenge: Existing approaches for text-based event prediction are limited in quality due to dynamic nature of international relations and conflicting economic dynamics.
Approach: They propose a novel dataset that leverages the advanced reasoning capabilities of large-language models to address these limitations.
Outcome: The proposed dataset features high-quality scoring labels generated through advanced prompt modeling and rigorously validated by domain experts in political science.
Massively Multi-Lingual Event Understanding: Extraction, Visualization, and Search (2023.acl-demo)

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Challenge: Using only English training data, ISI-Clear makes global events available on-demand in 100 languages . Using a fixed task, events may still shift from day to day .
Approach: They propose a cross-lingual zero-shot event extraction system that makes global events available on-demand in 100 languages.
Outcome: The proposed system can extract events from non-English documents in 100 languages.
HumSet: Dataset of Multilingual Information Extraction and Classification for Humanitarian Crises Response (2022.findings-emnlp)

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Challenge: During humanitarian crises, a quick and accurate analysis of relevant data is critical to a timely and effective response.
Approach: They introduce and release a multilingual dataset of humanitarian response documents annotated by experts in the humanitarian response domain.
Outcome: The proposed dataset provides documents in three languages and covers a variety of humanitarian crises from 2018 to 2021 across the globe.

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