| Challenge: | Existing methods for sub-event detection do not account for sequential nature of social media streams. |
| Approach: | They propose to use a neural sequence architecture that explicitly accounts for the chronological order of posts to improve sub-event detection. |
| Outcome: | The proposed method outperforms a graph-based state-of-the-art method for binary sub-event detection (2.7% micro-F1 improvement) it also outperformed a recurrent neural network model on the posts sequence level for labeled sub- events (2.4% bin-level improvement). |
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| Challenge: | Recent work on event detection from tweets has focused on localized events or breaking news only. |
| Approach: | They propose to split tweets into segments, extract bursty segments, cluster them, summarize them. |
| Outcome: | The proposed system can detect newsworthy events occurring at different locations of the world from a wide range of categories. |
Detecting Subevents using Discourse and Narrative Features (P19-1)
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| Challenge: | Existing models for detecting events as subevents have been developed for analyzing textual understanding. |
| Approach: | They propose a supervised model that automatically identifies when one event is a subevent of another. |
| Outcome: | The proposed model outperforms previous systems on two annotated corpora with event hierarchies, achieving 0.74 BLANC F1 on the Intelligence Community corpus and 0.70 F1 for the HiEve corpus, respectively a 15 and 5 percentage point improvement over previous models. |
Event Detection as Graph Parsing (2021.findings-acl)
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| Challenge: | Existing approaches to event detection focus on using syntactic dependency structures or external knowledge to boost the performance. |
| Approach: | They propose a graph parsing problem that explicitly models multiple event correlations and utilizes rich information conveyed by event type and subtype. |
| Outcome: | The proposed model outperforms existing models on the public ACE2005 dataset by 4.2% on the dataset. |
Exploring a Unified Sequence-To-Sequence Transformer for Medical Product Safety Monitoring in Social Media (2021.findings-emnlp)
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| Challenge: | Adverse Events (AEs) are harmful events resulting from the use of medical products. |
| Approach: | They propose a model that combines sequence-to-sequence learning with language transfer capabilities to improve model robustness. |
| Outcome: | The proposed approach achieves strong performance over baselines on English benchmarks. |
Zero-Shot Sequence Labeling: Transferring Knowledge from Sentences to Tokens (N18-1)
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| Challenge: | Recent work has used attention weights to visualize the focus of neural models in input data. |
| Approach: | They propose to use attention-based visualization techniques to infer token-level labels from a network trained only on sentence-level labeling. |
| Outcome: | The proposed approach outperforms gradient-based methods on four datasets and is expected to outperfect supervised methods. |
Three Stream Based Multi-level Event Contrastive Learning for Text-Video Event Extraction (2023.emnlp-main)
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| Challenge: | Existing methods for event extraction ignore motion representations in videos and are misguided by background noise. |
| Approach: | They propose a text-video based multimodal event extraction framework that integrates video appearance features and motion representations with video appearance. |
| Outcome: | The proposed framework outperforms the state-of-the-art methods in the event extraction field. |
Event Detection with Neural Networks: A Rigorous Empirical Evaluation (D18-1)
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| Challenge: | Neural network models have been the most successful for event detection, but they ignore syntactic relationships in the text. |
| Approach: | They propose a GRU-based model that combines syntactic information along with temporal structure through an attention mechanism. |
| Outcome: | The proposed model is competitive with existing models on a ACE2005 dataset. |
Biomedical Event Extraction as Sequence Labeling (2020.emnlp-main)
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| Challenge: | Empirical results show that BeeSL’s speed and accuracy makes it a viable approach for large-scale real-world scenarios. |
| Approach: | They propose a joint end-to-end neural information extraction model that recasts the task as sequence labeling and jointly models intermediate tasks via multi-task learning. |
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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. |
| Outcome: | The proposed model outperforms ten strong baselines on ACE2005 and KBP2015 datasets. |
A French Corpus for Event Detection on Twitter (2020.lrec-1)
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| Challenge: | Existing datasets may have different definitions of event or topic, which leads to inconsistent results. |
| Approach: | They present a corpus annotated for event detection tasks consisting of 38 million tweets in French and 130,000 manually annotating tweets as related or unrelated to a given event. |
| Outcome: | The proposed method performs best on 38 million tweets in French and another publicly available dataset of tweets. |