Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation (1809.09078v2)

Abstract: Event extraction is of practical utility in natural language processing. In the real world, it is a common phenomenon that multiple events existing in the same sentence, where extracting them are more difficult than extracting a single event. Previous works on modeling the associations between events by sequential modeling methods suffer a lot from the low efficiency in capturing very long-range dependencies. In this paper, we propose a novel Jointly Multiple Events Extraction (JMEE) framework to jointly extract multiple event triggers and arguments by introducing syntactic shortcut arcs to enhance information flow and attention-based graph convolution networks to model graph information. The experiment results demonstrate that our proposed framework achieves competitive results compared with state-of-the-art methods.

An Overview of "Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation"

The paper "Jointly Multiple Events Extraction via Attention-based Graph Information Aggregation" introduces a novel framework, termed JMEE, for the task of event extraction in natural language processing. Event extraction is critical for understanding a document by identifying and categorizing event triggers and their arguments. The framework targets the multifaceted challenge posed by multiple events co-occurring in a sentence, which demands effective modeling beyond sequential processing techniques. The proposed JMEE framework leverages syntactic shortcut arcs and graph convolution networks (GCNs) to enhance information flow and captures dependencies across events using attention mechanisms.

Methodology

The JMEE framework is developed to jointly extract multiple event triggers and their corresponding arguments. The architecture of JMEE is based on four principal modules:

1. Word Representation: Each token in a sentence is represented using pre-trained word embeddings, part-of-speech (POS) tags, positional embeddings, and entity type embeddings. This multi-faceted representation forms the input to subsequent processing.
2. Syntactic Graph Convolution Network: To address the limitations of sequential models like RNNs in capturing long-range dependencies, this module utilizes GCNs with shortcut arcs derived from syntactic structures. The syntactic graphs allow for reduced hops between tokens, enabling more efficient contextual information aggregation.
3. Self-Attention Trigger Classification: This component employs a self-attention mechanism to model interactions between potential event triggers within the same sentence. This is pivotal for considering the associations between triggers, which traditional max-pooling approaches may overlook.
4. Argument Classification: This module evaluates each trigger-entity pair to determine the role of entities within identified events using a joint prediction process optimized via a biased loss function to compensate for data imbalance.

Results and Discussion

The JMEE framework is evaluated on the ACE 2005 dataset, demonstrating its competitive performance relative to state-of-the-art methods such as JRNN and DMCNN. The results show that JMEE achieves superior F1 scores in both trigger classification and role identification tasks. Notably, JMEE excels in scenarios where multiple events co-occur in a single sentence, highlighting the effectiveness of graph-based and attention mechanisms in such complex contexts.

In particular, the employment of syntactic graphs via dependency parses as shortcuts provides a significant advantage in mitigating the inefficiencies of sequential dependency capture by RNNs. Moreover, the use of self-attention enhances the framework's ability to disambiguate and accurately classify multiple triggers and their interactions. The experimental evaluations reaffirm the framework's robustness and adaptability to nuanced event interdependencies.

Implications and Future Directions

The implications of this research are substantial for both practical and theoretical developments in NLP. Practically, JMEE can enhance systems requiring nuanced understanding and extraction of complex event structures, such as information aggregation systems and automated reporting. Theoretically, the synthesis of graph-based structures and attention mechanisms offers a potent direction for future research in semantic understanding and interpretation.

Looking forward, an interesting avenue would involve extending the model's ability to integrate document-level information with the sentence-level extraction processes, thus potentially enhancing context comprehension further. Moreover, harnessing the potential of arguments playing various roles across events could lead to even more refined models of event interplay.

In conclusion, the JMEE framework presents a significant stride in the domain of event extraction, particularly addressing the challenges posed by co-occurring and interacting events within sentences. By capitalizing on advanced graph and attention-based methods, it lays a foundational pathway for enriched semantic processing in natural language understanding.