| Challenge: | Using a short text description of an event, we can extract relevant sentences from Wikipedia and apply a combination of task-specific and general-purpose feature embeddings for the classification. |
| Approach: | They propose to use Wikipedia sentences to extract relevant sentences and apply feature embeddings to the task. |
| Outcome: | The proposed model outperforms the historical event ordering task and the event focus time task in the literature. |
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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. |
| Approach: | This tutorial will provide an introduction to various methods for automating events and their relations, and a wide range of NLU and commonsense understanding tasks. |
| Outcome: | This tutorial will provide an introduction to various methods for automating extraction, conceptualization and prediction of events and their relations, and a wide range of NLU and commonsense understanding tasks. |
Event Extraction as Machine Reading Comprehension (2020.emnlp-main)
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| Challenge: | Event extraction (EE) is a crucial information extraction task that aims to extract event information in texts. |
| Approach: | They propose a new learning paradigm for event extraction by explicitly casting it as a machine reading comprehension problem. |
| Outcome: | The proposed model achieves state-of-the-art performance on the data-scarce scenario, achieving 49.8% in F1 for event argument extraction with only 1% data, compared with 2.2% of the previous method. |
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. |
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TextEE: Benchmark, Reevaluation, Reflections, and Future Challenges in Event Extraction (2024.findings-acl)
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Kuan-Hao Huang, I-Hung Hsu, Tanmay Parekh, Zhiyu Xie, Zixuan Zhang, Prem Natarajan, Kai-Wei Chang, Nanyun Peng, Heng Ji
| Challenge: | Recent studies suggest that event extraction evaluations may not accurately reflect the true performance. |
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| Outcome: | The proposed benchmarks show that they struggle to achieve satisfactory performance. |
EventWiki: A Knowledge Base of Major Events (L18-1)
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| Challenge: | Existing knowledge bases focus on static entities such as people, locations and organizations. |
| Approach: | They propose a new knowledge base resource called EventWiki which concentrates on major events . they show that EventWiki is a very useful resource for information extraction regarding events in NLP . |
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Financial Event Extraction Using Wikipedia-Based Weak Supervision (D19-51)
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Liat Ein-Dor, Ariel Gera, Orith Toledo-Ronen, Alon Halfon, Benjamin Sznajder, Lena Dankin, Yonatan Bilu, Yoav Katz, Noam Slonim
| Challenge: | Existing methods for detecting financial and economic events from text have relied on a knowledge-base of financial events, or corresponding financial figures. |
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Multilingual Event Extraction from Historical Newspaper Adverts (2023.acl-long)
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| Challenge: | Developing NLP methods for historical corpora is difficult, as only domain experts can label them . off-the-shelf models are trained on modern language texts, rendering them weaker for historical documents . |
| Approach: | They propose to use an annotated newspaper dataset to extract historical data from a novel domain of texts. |
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Event Linking: Grounding Event Mentions to Wikipedia (2023.eacl-main)
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| Challenge: | a new task for natural language understanding is called Event Linking . the context where an event is mentioned lacks the details of this event . |
| Approach: | They propose a new task to link an article's event mention to the most appropriate Wikipedia page . they collect a training set from Wikipedia and evaluate two models to test the task . |
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ESTER: A Machine Reading Comprehension Dataset for Reasoning about Event Semantic Relations (2021.emnlp-main)
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| Challenge: | Recent event-centric reading comprehension datasets focus mostly on event arguments or temporal relations. |
| Approach: | They propose a machine reading comprehension dataset that leverages natural language queries to reason about the five most common event semantic relations. |
| Outcome: | The proposed dataset shows that current SOTA systems achieve 22.1%, 63.3% and 83.5% for token-based exact-match, **F1** and event-based **HIT@1** scores. |
Revisiting Classical Chinese Event Extraction with Ancient Literature Information (2025.acl-long)
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| Challenge: | Existing studies on classical Chinese event extraction focus on grafting the complex modeling from English or modern Chinese works, neglecting the unique characteristic of this language. |
| Approach: | They propose a Literary Vision-Language Model (VLM) for classical Chinese event extraction . they integrate annotations, historical background and character glyphs to capture the inner- and outer-context information from the sequence. |
| Outcome: | The proposed model can capture the inner- and outer-context information at nearly zero cost. |