Extracting Nested Events by Recognizing Pivot Elements

Core Concepts

The core message of this paper is that recognizing pivot elements, which simultaneously act as arguments of outer-nest events and as triggers of inner-nest events, is crucial for effectively extracting nested event structures.

Abstract

The paper proposes a new model called PerNee for the Nested Event Extraction (NEE) task. NEE aims to extract complex event structures where an event contains other events as its arguments recursively. The key challenge in NEE is the recognition of Pivot Elements (PEs), which have dual identities as both arguments of outer-nest events and triggers of inner-nest events.
The PerNee model addresses this challenge by:

First recognizing the triggers of both inner-nest and outer-nest events using a trigger recognizer.
Then recognizing the PEs by classifying the relation type between trigger pairs, rather than treating PEs as regular arguments.
Utilizing prompt learning to incorporate information from both event types and argument roles for better trigger and argument representations.

To support the NEE task in the generic domain, the paper also introduces a new dataset called ACE2005-Nest, which systematically categorizes nested events beyond the medical domain.
Experimental results demonstrate that PerNee consistently outperforms existing baselines on ACE2005-Nest, Genia11, and Genia13 datasets. Ablation studies further validate the effectiveness of the PE recognizer module and the use of prompts.

Stats

"pay" is a pivot element that serves as the trigger of the inner-nest event Transfer-Ownership and as an argument of the outer-nest event Intention.
ACE2005-Nest contains 14 event types that can introduce nested structures in the generic domain, while Genia11 and Genia13 only have 3 and 5 such event types, respectively.
In ACE2005-Nest, approximately 25% of the sentences with events contain nested events, while in Genia11 and Genia13, the proportion is 39% and 49%, respectively.

Quotes

"Nested events involve a kind of Pivot Elements (PEs) that simultaneously act as arguments of outer-nest events and as triggers of inner-nest events, and thus connect them into nested structures."
"The key for the NEE task is to recognize this kind of PEs."

Key Insights Distilled From

Nested Event Extraction upon Pivot Element Recogniton

by Weicheng Ren... at arxiv.org 04-04-2024

https://arxiv.org/pdf/2309.12960.pdf

Deeper Inquiries

How can the PerNee model be extended to handle events with more complex structures, such as multi-level nested events?

To extend the PerNee model to handle events with more complex structures, such as multi-level nested events, several modifications and enhancements can be implemented:

Hierarchical Event Representation: The model can be adapted to represent events at different levels of hierarchy. This would involve capturing not only the immediate nested events but also events nested within those nested events.

Recursive Parsing: Implementing a recursive parsing mechanism can help the model to iteratively identify and extract nested events at multiple levels. This recursive approach would allow the model to handle complex event structures effectively.

Enhanced Trigger and Argument Recognition: Improving the trigger and argument recognition modules to be more robust and accurate in identifying triggers and arguments at different levels of nesting. This would involve refining the feature representations and training strategies to handle multi-level nested events.

Graph-based Event Representation: Utilizing a graph-based representation for events can help in capturing the relationships and dependencies between events at different levels of nesting. This graph structure can be dynamically updated as new events are identified during the extraction process.

Beam Search Optimization: Enhancing the beam search algorithm used in the structure decoder to efficiently explore and decode multi-level nested event structures. This optimization can help in finding the most optimal event hierarchies within the input text.

By incorporating these enhancements, the PerNee model can be extended to effectively handle events with more complex structures, including multi-level nested events.

How can the nested event schema discovery process be automated or enhanced to cover a broader range of event types that can introduce nested structures in the generic domain?

To automate and enhance the nested event schema discovery process for covering a broader range of event types in the generic domain, the following strategies can be employed:

Semantic Resources Integration: Integrate a wide range of semantic resources, such as WordNet, FrameNet, and other domain-specific ontologies, to enrich the event type and argument role definitions. This integration can provide a comprehensive understanding of event semantics and facilitate the discovery of diverse event types.

Machine Learning Techniques: Utilize machine learning techniques, such as clustering algorithms and topic modeling, to automatically identify and categorize event types based on the textual data. These techniques can help in uncovering hidden patterns and relationships among events, leading to the discovery of new event types.

Active Learning and Semi-Supervised Learning: Implement active learning strategies to interactively label and annotate data samples that are uncertain or informative. Additionally, leverage semi-supervised learning approaches to utilize both labeled and unlabeled data for event type discovery, thereby expanding the coverage of event types.

Natural Language Processing (NLP) Pipelines: Develop NLP pipelines that incorporate entity recognition, relation extraction, and event extraction modules to extract and analyze textual data comprehensively. By integrating these modules, the system can automatically identify and categorize events with nested structures.

Domain Adaptation Techniques: Apply domain adaptation techniques to transfer knowledge from existing datasets to the generic domain. By adapting pre-trained models and knowledge from specific domains to the generic domain, the system can better understand and categorize a broader range of event types.

By implementing these strategies, the nested event schema discovery process can be automated and enhanced to cover a wider spectrum of event types that introduce nested structures in the generic domain.