Joint Extraction of Events and Entities within a Document Context (1609.03632v1)

Abstract: Events and entities are closely related; entities are often actors or participants in events and events without entities are uncommon. The interpretation of events and entities is highly contextually dependent. Existing work in information extraction typically models events separately from entities, and performs inference at the sentence level, ignoring the rest of the document. In this paper, we propose a novel approach that models the dependencies among variables of events, entities, and their relations, and performs joint inference of these variables across a document. The goal is to enable access to document-level contextual information and facilitate context-aware predictions. We demonstrate that our approach substantially outperforms the state-of-the-art methods for event extraction as well as a strong baseline for entity extraction.

• The paper presents a novel method for the joint extraction of events and entities by leveraging document-level context, addressing limitations of sentence-level analysis.
• The approach models dependencies within individual events and between events across a document, integrating these into a joint inference framework.
• Results show significant improvements in event trigger identification, classification, and argument extraction, outperforming state-of-the-art methods and enhancing related NLP tasks.

Joint Extraction of Events and Entities within a Document Context

The paper presents a methodology dedicated to the joint extraction of events and entities within a document context, primarily addressing the limitations observed in traditional information extraction systems that treat events and entities separately and often at a sentence level. The authors argue that events and entities are intrinsically linked—entities typically participate in events, and the extraction of such information benefits significantly from considering document-wide context rather than being confined to individual sentences.

Problem Definition and Methodology

In tackling the challenges of event and entity extraction, this work relies on the ACE definitions for entities and events, focusing on identifying entity mentions, event triggers, and event arguments. The novelty of this approach lies in its ability to model dependencies amongst variables associated with events, entities, and their interrelations across an entire document. The approach is streamlined into three computationally feasible subproblems: learning the dependencies within single events, between events across a document, and entity extraction itself.

The model is structured into two primary components:

• Within-Event Structures: This component captures the dependencies between event triggers and their arguments, as well as the relationship between semantic roles and entity types.
• Event-Event Relations: This considers the likelihood of certain events co-occurring within a document, drawing connections that aid in refining the context and meaning extracted.

These components are ultimately integrated into a joint inference framework that refines predictions by sharing information amongst interconnected sub-tasks, thereby offering a holistic, context-aware extraction of events and entities.

Results and Implications

The research demonstrates substantial improvements in both event trigger identification and classification, as well as in event argument extraction. The approach outperforms state-of-the-art methods—showcased through significant increases in precision, recall, and F1 scores across different extraction tasks. Specifically, the integration of document-level context enhances the robustness of the inference model, leading to more accurate identification and classification of event-related information as well as improved entity extraction.

Furthermore, the joint modeling of events and entities highlights the symbiotic relationship between the two, where improved understanding of one enhances the accuracy of the other. This integrated framework not only refines event extraction but also boosts performance in related tasks such as knowledge base construction, information retrieval, and potentially real-time applications like news summarization.

Future Directions

Advancements suggested by this research pave pathways for several future endeavors:

• Incorporation of Coreference Resolution: The paper identifies the role of coreference in enhancing extraction accuracy. Future work could deeply integrate coreference resolution into the joint model which would aid in the consistent and coherent extraction of events and entities spread across different document parts.
• Event Relations: There is significant potential in expanding the event-event relation model to incorporate causal and temporal dynamics between events, especially in more complex texts where chronology and logic significantly impact interpretation.
• Cross-Domain Applications: While this research focuses on ACE data, applications across various domains like biomedical data could further validate the model's adaptability and efficiency.

In conclusion, this research provides a promising stride towards more efficient and accurate event and entity extraction by leveraging document-level context and modeling interdependencies, setting a strong precedent for subsequent developments in the field of information extraction and natural language processing.