Entity, Relation, and Event Extraction with Contextualized Span Representations (1909.03546v2)

Abstract: We examine the capabilities of a unified, multi-task framework for three information extraction tasks: named entity recognition, relation extraction, and event extraction. Our framework (called DyGIE++) accomplishes all tasks by enumerating, refining, and scoring text spans designed to capture local (within-sentence) and global (cross-sentence) context. Our framework achieves state-of-the-art results across all tasks, on four datasets from a variety of domains. We perform experiments comparing different techniques to construct span representations. Contextualized embeddings like BERT perform well at capturing relationships among entities in the same or adjacent sentences, while dynamic span graph updates model long-range cross-sentence relationships. For instance, propagating span representations via predicted coreference links can enable the model to disambiguate challenging entity mentions. Our code is publicly available at https://github.com/dwadden/dygiepp and can be easily adapted for new tasks or datasets.

• The paper presents a unified framework that leverages contextual embeddings and span graph propagation to achieve state-of-the-art information extraction performance.
• It integrates entity recognition, relation extraction, and event extraction into a single multi-task system, reducing errors by up to 27.9%.
• The approach demonstrates practical improvements across diverse domains and paves the way for future advances in NLP contextualization techniques.

Entity, Relation, and Event Extraction with Contextualized Span Representations

Overview

This paper presents a comprehensive framework for information extraction (IE) tasks using contextualized span representations. It addresses named entity recognition, relation extraction, and event extraction within a unified multi-task setup. The framework leverages contextualized embeddings and span graph propagation to integrate both local and global contexts, achieving state-of-the-art results across multiple datasets spanning various domains.

Framework and Methodology

The proposed framework achieves impressive results by enumerating, refining, and scoring text spans that encapsulate both sentence-level and cross-sentence context. Key components include:

1. Contextual LLMs: Utilizing models like BERT, the system captures intra-sentence relationships effectively, while adapting to cross-sentence dependencies through dynamic span updates.
2. Span Graph Propagation: A graph-based representation is constructed dynamically, where edges represent context-sharing relationships between spans. This technique is particularly effective in propagating coreference links, enhancing the disambiguation of entity mentions.
3. Task-Specific Components: Beyond adding another layer of complexity with event extraction tasks, the framework integrates event trigger and argument predictions efficiently, even in the absence of gold entity labels, using predicted entity mentions as arguments.

Quantitative Results

The framework achieves state-of-the-art performance across three IE tasks with notable improvements:

• Named Entity Recognition: Enhanced performance with error reductions up to 27.9%.
• Relation Extraction: Significant improvements noted particularly in scientific domains due to effective contextualization.
• Event Extraction: Introduces novel methods for integrating context via dynamic graph propagation, although with mixed results for specific subtasks such as argument identification.

Implications

The results underscore the framework's capacity to handle complex information extraction tasks by ensuring more accurate entity recognition and relation prediction through sophisticated contextualization techniques. The ability to capture semantic nuances across sentences has profound implications for enhancing NLP applications in diverse domains, such as scientific literature and news articles.

Future Directions

The paper points towards several avenues for advancement:

• Model Extensions: Further development could involve expanding the architecture to other NLP tasks, potentially integrating additional types of contextualization.
• Higher-Order Interactions: Exploring the capture of complex interactions and relationships, especially within event extraction scenarios, could offer substantial benefits.
• In-Domain Pretraining: Emphasizes the importance of domain-specific pretraining, as evidenced by experiments with SciBERT, suggesting similar strategies could be applied to other specialized fields.

Conclusion

This paper presents a robust IE framework leveraging contextual embeddings and span graph propagation, achieving state-of-the-art performance across multiple benchmarks. It enhances our understanding of how advanced modeling techniques can improve the extraction of complex semantic relationships, paving the way for future developments in NLP methodologies. The availability of the codebase opens possibilities for further exploration and adaptation to a variety of information extraction tasks and datasets.