Master of Disaster: A Disaster-Related Event Monitoring System From News Streams
- URL: http://arxiv.org/abs/2406.09323v1
- Date: Thu, 13 Jun 2024 17:01:28 GMT
- Title: Master of Disaster: A Disaster-Related Event Monitoring System From News Streams
- Authors: Junbo Huang, Ricardo Usbeck,
- Abstract summary: The need for a disaster-related event monitoring system has arisen due to the societal and economic impact caused by the increasing number of severe disaster events.
We demonstrate our open-source event monitoring system, Master of Disaster (MoD), which receives news streams, extracts event information, links extracted information to a knowledge graph (KG), and discriminates event instances visually.
- Score: 6.731053352452566
- License:
- Abstract: The need for a disaster-related event monitoring system has arisen due to the societal and economic impact caused by the increasing number of severe disaster events. An event monitoring system should be able to extract event-related information from texts, and discriminates event instances. We demonstrate our open-source event monitoring system, namely, Master of Disaster (MoD), which receives news streams, extracts event information, links extracted information to a knowledge graph (KG), in this case Wikidata, and discriminates event instances visually. The goal of event visualization is to group event mentions referring to the same real-world event instance so that event instance discrimination can be achieved by visual screening.
Related papers
- Grounding Partially-Defined Events in Multimodal Data [61.0063273919745]
We introduce a multimodal formulation for partially-defined events and cast the extraction of these events as a three-stage span retrieval task.
We propose a benchmark for this task, MultiVENT-G, that consists of 14.5 hours of densely annotated current event videos and 1,168 text documents, containing 22.8K labeled event-centric entities.
Results illustrate the challenges that abstract event understanding poses and demonstrates promise in event-centric video-language systems.
arXiv Detail & Related papers (2024-10-07T17:59:48Z) - Improving Event Definition Following For Zero-Shot Event Detection [66.27883872707523]
Existing approaches on zero-shot event detection usually train models on datasets annotated with known event types.
We aim to improve zero-shot event detection by training models to better follow event definitions.
arXiv Detail & Related papers (2024-03-05T01:46:50Z) - Detecting Anomalous Events in Object-centric Business Processes via
Graph Neural Networks [55.583478485027]
This study proposes a novel framework for anomaly detection in business processes.
We first reconstruct the process dependencies of the object-centric event logs as attributed graphs.
We then employ a graph convolutional autoencoder architecture to detect anomalous events.
arXiv Detail & Related papers (2024-02-14T14:17:56Z) - Type-aware Decoding via Explicitly Aggregating Event Information for
Document-level Event Extraction [11.432496741340334]
Document-level event extraction faces two main challenges: arguments-scattering and multi-event.
This paper proposes a novel-based Explicitly Aggregating(SEA) model to address these limitations.
SEA aggregates event information into event type and role representations, enabling the decoding of event records based on specific type-aware representations.
arXiv Detail & Related papers (2023-10-16T15:10:42Z) - Event Causality Extraction with Event Argument Correlations [13.403222002600558]
Event Causality Extraction aims to extract cause-effect event causality pairs from plain texts.
We propose a method with a dual grid tagging scheme to capture the intra- and inter-event argument correlations for ECE.
arXiv Detail & Related papers (2023-01-27T09:48:31Z) - The Art of Prompting: Event Detection based on Type Specific Prompts [28.878630198163556]
We develop a unified framework to incorporate the event type specific prompts for supervised, few-shot, and zero-shot event detection.
Our framework shows up to 24.3% F-score gain over the previous state-of-the-art baselines.
arXiv Detail & Related papers (2022-04-14T21:28:50Z) - Robust Event Classification Using Imperfect Real-world PMU Data [58.26737360525643]
We study robust event classification using imperfect real-world phasor measurement unit (PMU) data.
We develop a novel machine learning framework for training robust event classifiers.
arXiv Detail & Related papers (2021-10-19T17:41:43Z) - Integrating Deep Event-Level and Script-Level Information for Script
Event Prediction [60.67635412135681]
We propose a Transformer-based model, called MCPredictor, which integrates deep event-level and script-level information for script event prediction.
The experimental results on the widely-used New York Times corpus demonstrate the effectiveness and superiority of the proposed model.
arXiv Detail & Related papers (2021-09-24T07:37:32Z) - EventPlus: A Temporal Event Understanding Pipeline [12.313545429119651]
EventPlus is a temporal event understanding pipeline that integrates various state-of-the-art event understanding components.
We make EventPlus publicly available to facilitate event-related information extraction and downstream applications.
arXiv Detail & Related papers (2021-01-13T08:00:50Z) - Event-Related Bias Removal for Real-time Disaster Events [67.2965372987723]
Social media has become an important tool to share information about crisis events such as natural disasters and mass attacks.
Detecting actionable posts that contain useful information requires rapid analysis of huge volume of data in real-time.
We train an adversarial neural model to remove latent event-specific biases and improve the performance on tweet importance classification.
arXiv Detail & Related papers (2020-11-02T02:03:07Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.