Connecting the Dots: Document-level Neural Relation Extraction with Edge-oriented Graphs
The presented research addresses the domain of document-level relation extraction (RE), focusing on improving the extraction process by using edge-oriented graph neural networks. Unlike prior node-based methods that primarily depend on target node representations for pair formation, the proposed approach utilizes edge representations in a graph structure to encapsulate document-level relations efficiently.
Document-level RE poses significant challenges as many natural language relationships transpire across multiple sentences, necessitating inference techniques that consider the broader textual context. The existing systems using standard graph-based approaches generally treat words as nodes and relations as edges, mainly operating on node representations and thus limiting their inferential capacity across document-level dependencies.
In response to these challenges, this paper introduces an edge-oriented graph neural model that constructs a document-level graph leveraging different types of nodes and edges to better capture interdependencies. The model employs a multi-instance learning strategy to discern intra- and inter-sentence relations by learning edge paths between nodes, rather than focusing solely on node features. Therefore, it creates an enhanced pathway for inference that is crucial for accurate document-level RE.
The experimental evaluation involves two substantial biomedical datasets: one dataset involving chemical-disease interactions and another concerning gene-disease associations. The results indicate that the proposed approach outperforms established baseline models, notably demonstrating an ability to undertake both intra- and inter-sentence RE with increased precision. For instance, on the CDR dataset, the edge-oriented approach achieved a 1.3 percentage points improvement over existing methods without external knowledge integration. Intriguingly, this model also reveals that inter-sentence context can enhance intra-sentence relation predictions, indicating a beneficial overlap in document-level RE tasks.
Key architectural innovations of the model lie in its graph construction, which includes heterogeneous nodes for mentions, entities, and sentences, connected by heuristic-driven edges such as Mention-Mention (MM), Mention-Entity (ME), and Sentence-Sentence (SS), among others. Notably, the methodology introduces an iterative inference mechanism that layers edge representations incrementally, facilitating robust conceptual pair recognition throughout documents.
The paper’s contributions are multifaceted. Firstly, it presents a significant divergence from traditional node-focused strategies by emphasizing edge-centric graph construction and learning. Secondly, it demonstrates effectiveness in state-of-the-art performance on a manually annotated dataset in a domain where alias variability is substantial. Lastly, the analysis showcases that document-level graphs can effectively synthesize dependencies beneficial for RE tasks.
Future research directions suggested by this paper include enhancing the inference mechanics and potentially integrating additional sources of document-level information into the graph structure. Collectively, these advancements may expand the applicability of RE models across more diversified domains and complex narrative structures.
The research is foundational in suggesting that an edge-oriented perspective potentially opens new avenues in the field of document-level RE, providing a template for future examination and application within and beyond biomedical contexts.