Unsupervised Episode Detection for Large-Scale News Events

2024-08-09

arXiv.org

AI Summary

Existing automated event detection methods suffer from limited interpretability and poor adaptability to large-scale critical events, failing to capture “episodes”—semantically coherent subunits composed of core entities, actions, and spatiotemporal contexts. This paper introduces, for the first time, the unsupervised episode detection task, aiming to automatically identify such natural narrative fragments from news corpora. To address key challenges—including the absence of explicit spatiotemporal markers and the inadequacy of semantic similarity–based clustering—we propose EpiMine, a two-stage framework: (1) discriminative term migration to detect episode boundaries, and (2) large language model–driven reasoning and clustering over candidate fragments. Evaluated on three real-world, human-annotated datasets, EpiMine achieves an average 59.2% performance gain over all baselines. Our approach establishes a novel, interpretable, and scalable paradigm for critical event modeling.

Technology Category

• Data Mining & Knowledge Management: Anomaly/Outlier Detection
• Natural Language Processing: Fact-Checking / Misinformation Detection (NLP Focus)
• Knowledge Representation and Reasoning: Geometric, Spatial, and Temporal Reasoning

Application Category

• Web Mining and Content Analysis: Topic discovery and tracking
• Graph Algorithms and Modeling for the Web: Representation, reconstruction, and subgraph or motif discovery in Web-related graphs
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Episodic structures are inherently interpretable and adaptable to evolving large-scale key events. However, state-of-the-art automatic event detection methods overlook event episodes and, therefore, struggle with these crucial characteristics. This paper introduces a novel task, episode detection, aimed at identifying episodes from a news corpus containing key event articles. An episode describes a cohesive cluster of core entities (e.g.,"protesters","police") performing actions at a specific time and location. Furthermore, an episode is a significant part of a larger group of episodes under a particular key event. Automatically detecting episodes is challenging because, unlike key events and atomic actions, we cannot rely on explicit mentions of times and locations to distinguish between episodes or use semantic similarity to merge inconsistent episode co-references. To address these challenges, we introduce EpiMine, an unsupervised episode detection framework that (1) automatically identifies the most salient, key-event-relevant terms and segments, (2) determines candidate episodes in an article based on natural episodic partitions estimated through shifts in discriminative term combinations, and (3) refines and forms final episode clusters using large language model-based reasoning on the candidate episodes. We construct three diverse, real-world event datasets annotated at the episode level. EpiMine outperforms all baselines on these datasets by an average 59.2% increase across all metrics.

Problem

• Detecting cohesive episodes in news for key events
• Overcoming lack of explicit markers in episode detection
• Improving interpretability and adaptability in event detection

Innovation

• Unsupervised episode detection via term shifts
• LLM synergy for refining episode interpretations
• EpiMine framework for cohesive episode identification