How Concrete Do We Get Telling Stories?

Area of Science:

• Natural Language Processing (NLP)
• Artificial Intelligence (AI)
• Cognitive Science

Background:

• Language is inherently abstract, requiring episodic experience to form concrete mental representations.
• Individual episodic knowledge leads to diverse interpretations of the same narrative.
• Textual accounts often omit crucial details necessary for complete event reconstruction.

Purpose of the Study:

• To investigate the challenges in automatically modeling event descriptions and instances across diverse news articles.
• To analyze how variations in event description granularity affect the establishment of event identity and relations.
• To highlight the difficulties machines face in understanding events due to missing contextual knowledge.

Main Methods:

• Experimental approach to automatically model event descriptions and instances.
• Analysis of event information scattered across multiple news articles.
• Examination of the degree of abstraction in event descriptions.

Main Results:

• Event information is fragmented within texts and varies significantly in its level of detail.
• Establishing precise event identity and relations is challenging due to this scattered and abstract information.
• Granularity of event descriptions appears linked to pragmatic communicative strategies.

Conclusions:

• Reconstructing events and their relations from text is complex due to language abstraction and missing world knowledge.
• Machines struggle to bridge the gap between textual information and the real-world knowledge needed for event understanding.
• The variability in event description granularity presents a multi-level challenge for automated event modeling.