Prompt-based Graph Model for Joint Liberal Event Extraction and Event Schema Induction

Core Concepts

Events are extracted liberally using a prompt-based graph model to discover event schemas simultaneously.

Abstract

1. Abstract:

   • Events describe state changes in entities.
   • Traditional event extraction relies on pre-defined schemas.
   • Liberal Event Extraction (LEE) aims to extract events and schemas simultaneously.

2. Approach:

   • Prompt-based model generates candidate triggers and arguments.
   • Heterogeneous event graphs encode structures within events.

3. Experiments:

   • Evaluated on TAC-KPB 2017 dataset with F1 scores.
   • PGLEE outperforms baselines in trigger identification, classification, argument identification, and classification.

4. Conclusion:

   • PGLEE achieves end-to-end event extraction and schema induction effectively.

Stats

"Experimental results prove that our approach achieves excellent performance with or without predefined event schemas."
"The automatically detected event schemas are proven high quality."

Quotes

"Liberal Event Extraction (LEE) aims to extract events and discover event schemas simultaneously."
"Our contributions focus on the LEE task and propose a novel prompt-based graph model PGLEE."

Key Insights Distilled From

Prompt-based Graph Model for Joint Liberal Event Extraction and Event Schema Induction

by Haochen Li,D... at arxiv.org 03-20-2024

https://arxiv.org/pdf/2403.12526.pdf

Deeper Inquiries

How can the prompt-based approach benefit other areas of natural language processing?

The prompt-based approach offers a versatile and efficient way to generate candidate triggers and arguments directly from input sentences without relying on external knowledge bases. This method can be applied in various natural language processing tasks such as information extraction, sentiment analysis, question answering, and summarization. By using prompts tailored to specific tasks, models can effectively capture relevant information from text data, leading to improved performance and flexibility across different NLP applications.

What are the potential drawbacks of relying solely on automatically detected event schemas?

While automatically detected event schemas offer advantages such as scalability and adaptability across domains, there are potential drawbacks to consider. One major concern is the risk of inaccuracies or misinterpretations in the detection process, which could lead to incorrect labeling of events or arguments. Additionally, automatic schema induction may overlook nuanced contextual cues that human experts could identify accurately. Moreover, without manual validation or supervision, there is a possibility of generating irrelevant or noisy event schemas that do not align with the intended task requirements.

How might the use of external knowledge bases enhance the performance of the PGLEE model?

External knowledge bases play a crucial role in enriching semantic understanding and improving model performance in event extraction tasks like those addressed by PGLEE. By leveraging external resources such as WordNet or VerbNet for semantic analysis and entity disambiguation during candidate generation stages, PGLEE can enhance its ability to identify accurate triggers and arguments within text data. Furthermore, incorporating domain-specific knowledge from external sources enables better alignment between extracted events and pre-existing schemas for more precise clustering results during schema induction processes. Overall, integrating external knowledge bases provides valuable context that enhances both feature interactions within events and overall model accuracy in liberal event extraction scenarios.