| Challenge: | Existing work on restricted domains and event annotation has limited coverage of events. |
| Approach: | They propose a distant supervision method that generates high-quality training data . they use a manually annotated corpus as a model to investigate events in various domains . |
| Outcome: | The proposed method outperforms supervised models in a manually annotated event corpus despite no direct supervision . |
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| Challenge: | Currently, few or no language processing tools or resources exist for most languages . a problem is that there is not enough available training data even in resource-rich languages if the task is complex. |
| Approach: | They propose to use a bilingual dictionary to train machine learning in a resource-poor language . they also explore adversarial training of bilingual word representations . |
| Outcome: | The proposed approach gives similar performance in event-type detection tasks. |
MAVEN: A Massive General Domain Event Detection Dataset (2020.emnlp-main)
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Xiaozhi Wang, Ziqi Wang, Xu Han, Wangyi Jiang, Rong Han, Zhiyuan Liu, Juanzi Li, Peng Li, Yankai Lin, Jie Zhou
| Challenge: | Existing datasets exhibit data scarcity and limited coverage of general-domain events. |
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Leveraging Information Redundancy of Real-World Data through Distant Supervision (2024.lrec-main)
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| Challenge: | Existing methods for annotation of health care notes are promising but they are limited due to privacy regulations. |
| Approach: | They propose a text labeling method that leverages the redundancy of temporal information in a data lake to create a large programmatically annotated corpus and train transformer models using distant supervision. |
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Treasures Outside Contexts: Improving Event Detection via Global Statistics (2021.emnlp-main)
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| Challenge: | Existing neural-based ED models are confused by changeable contexts during testing . we propose a system that extracts statistical event features from word-event cooccurrence frequencies . |
| Approach: | They propose to integrate a set of statistical event features from word-event co-occurrence frequencies into the training set to cooperate with contextual features. |
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Adversarial Training for Weakly Supervised Event Detection (N19-1)
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| Challenge: | Detecting and identifying events is an important subtask of event extraction. |
| Approach: | They build a large event-related candidate set with good coverage and apply an adversarial training mechanism to iteratively identify informative instances from the candidate set and filter out those noisy ones. |
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Cross-Domain Evaluation of Edge Detection for Biomedical Event Extraction (2020.lrec-1)
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| Challenge: | Biomedical event extraction systems are evaluated in-domain and on complete event structures only. |
| Approach: | They present a cross-domain study of edge detection for biomedical event extraction . they analyze differences between five existing gold standard corpora and provide a strong baseline model . |
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Leveraging Open Information Extraction for More Robust Domain Transfer of Event Trigger Detection (2024.findings-eacl)
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| Challenge: | Event detection is a crucial information extraction task in many domains, such as Wikipedia or news. |
| Approach: | They propose to use subject-object relations obtained from a rule-based open information extraction system to enhance domain transfer of trigger detection (TD) they combine this enhanced transfer with masked language modeling on the target domain, observing further TD transfer gains. |
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Towards Open Domain Event Trigger Identification using Adversarial Domain Adaptation (2020.acl-main)
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| Challenge: | supervised event trigger identification models can generalize better across domains . prior work focused on annotating specific categories of events or narratives from specific domains. |
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Improving Event Definition Following For Zero-Shot Event Detection (2024.acl-long)
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Zefan Cai, Po-Nien Kung, Ashima Suvarna, Mingyu Ma, Hritik Bansal, Baobao Chang, P. Jeffrey Brantingham, Wei Wang, Nanyun Peng
| Challenge: | Existing approaches on zero-shot event detection train models on datasets annotated with known event types and prompt them with unseen event definitions. |
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Open Domain Event Extraction Using Neural Latent Variable Models (P19-1)
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| Challenge: | Existing work on extracting events from news documents focuses on a set of pre-specified event types. |
| Approach: | They propose a latent variable neural model which is scalable to large corpus. |
| Outcome: | The proposed model performs better than the state-of-the-art method for event schema induction. |