Large Language Model Enhanced Clustering for News Event Detection

• URL: http://arxiv.org/abs/2406.10552v4
• Date: Sat, 6 Jul 2024 09:19:08 GMT
• Title: Large Language Model Enhanced Clustering for News Event Detection
• Authors: Adane Nega Tarekegn,
• Abstract summary: This paper presents an event detection framework that leverages Large Language Models (LLMs) combined with clustering analysis. The framework enhances event clustering through both pre-event detection tasks and post-event detection tasks. We introduce a novel Cluster Stability Assessment Index (CSAI) to assess the validity and robustness of clustering results.
• Score: 1.7094064195431142
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The news landscape is continuously evolving, with an ever-increasing volume of information from around the world. Automated event detection within this vast data repository is essential for monitoring, identifying, and categorizing significant news occurrences across diverse platforms. This paper presents an event detection framework that leverages Large Language Models (LLMs) combined with clustering analysis to detect news events from the Global Database of Events, Language, and Tone (GDELT). The framework enhances event clustering through both pre-event detection tasks (keyword extraction and text embedding) and post-event detection tasks (event summarization and topic labelling). We also evaluate the impact of various textual embeddings on the quality of clustering outcomes, ensuring robust news categorization. Additionally, we introduce a novel Cluster Stability Assessment Index (CSAI) to assess the validity and robustness of clustering results. CSAI utilizes multiple feature vectors to provide a new way of measuring clustering quality. Our experiments indicate that the use of LLM embedding in the event detection framework has significantly improved the results, demonstrating greater robustness in terms of CSAI scores. Moreover, post-event detection tasks generate meaningful insights, facilitating effective interpretation of event clustering results. Overall, our experimental results indicate that the proposed framework offers valuable insights and could enhance the accuracy in news analysis and reporting.

Related papers

• Unsupervised Social Event Detection via Hybrid Graph Contrastive Learning and Reinforced Incremental Clustering [17.148519270314313]
  We propose a novel unsupervised social media event detection method via hybrid graph contrastive learning and reinforced incremental clustering. We conduct comprehensive experiments to evaluate HCRC on the Twitter and Maven datasets.
  arXiv  Detail & Related papers  (2023-12-08T08:56:59Z)
• Token-Event-Role Structure-based Multi-Channel Document-Level Event Extraction [15.02043375212839]
  This paper introduces a novel framework for document-level event extraction, incorporating a new data structure called token-event-role. The proposed data structure enables our model to uncover the primary role of tokens in multiple events, facilitating a more comprehensive understanding of event relationships. The results demonstrate that our approach outperforms the state-of-the-art method by 9.5 percentage points in terms of the F1 score.
  arXiv  Detail & Related papers  (2023-06-30T15:22:57Z)
• Boosting Event Extraction with Denoised Structure-to-Text Augmentation [52.21703002404442]
  Event extraction aims to recognize pre-defined event triggers and arguments from texts. Recent data augmentation methods often neglect the problem of grammatical incorrectness. We propose a denoised structure-to-text augmentation framework for event extraction DAEE.
  arXiv  Detail & Related papers  (2023-05-16T16:52:07Z)
• Abnormal Event Detection via Hypergraph Contrastive Learning [54.80429341415227]
  Abnormal event detection plays an important role in many real applications. In this paper, we study the unsupervised abnormal event detection problem in Attributed Heterogeneous Information Network. A novel hypergraph contrastive learning method, named AEHCL, is proposed to fully capture abnormal event patterns.
  arXiv  Detail & Related papers  (2023-04-02T08:23:20Z)
• 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)
• Reliable Shot Identification for Complex Event Detection via Visual-Semantic Embedding [72.9370352430965]
  We propose a visual-semantic guided loss method for event detection in videos. Motivated by curriculum learning, we introduce a negative elastic regularization term to start training the classifier with instances of high reliability. An alternative optimization algorithm is developed to solve the proposed challenging non-net regularization problem.
  arXiv  Detail & Related papers  (2021-10-12T11:46:56Z)
• Learning Constraints and Descriptive Segmentation for Subevent Detection [74.48201657623218]
  We propose an approach to learning and enforcing constraints that capture dependencies between subevent detection and EventSeg prediction. We adopt Rectifier Networks for constraint learning and then convert the learned constraints to a regularization term in the loss function of the neural model.
  arXiv  Detail & Related papers  (2021-09-13T20:50:37Z)
• Adversarial Feature Augmentation and Normalization for Visual Recognition [109.6834687220478]
  Recent advances in computer vision take advantage of adversarial data augmentation to ameliorate the generalization ability of classification models. Here, we present an effective and efficient alternative that advocates adversarial augmentation on intermediate feature embeddings. We validate the proposed approach across diverse visual recognition tasks with representative backbone networks.
  arXiv  Detail & Related papers  (2021-03-22T20:36:34Z)

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.