Core Concepts
The author proposes a method that fuses constituency and dependency syntax to enhance document-level relation extraction, improving performance on various datasets.
Abstract
The content discusses the challenges of document-level relation extraction, introduces the FCDS model that combines dependency and constituency syntax, and presents experimental results demonstrating its effectiveness. The model outperforms existing methods by incorporating both types of syntax information to capture long-distance relations.
Key points include:
- Introduction to Document-Level Relation Extraction (DocRE)
- Challenges in inferring relations between entities in long sentences
- Utilization of pre-trained language models (PLMs) for downstream tasks
- Incorporation of dependency and constituency syntax in the FCDS model
- Detailed methodology involving text encoding, constituency tree construction, dependency graph construction, dynamic fusion, and classification
- Experimental results on three datasets showcasing improved performance over existing methods
- Ablation study highlighting the importance of different components in the FCDS model
The FCDS model demonstrates the significance of combining syntax information for more accurate document-level relation extraction.
Stats
"Experimental results on datasets from various domains demonstrate the effectiveness of the proposed method."
"Our model improves the IgnF1 score on the test set by 1.56% over the state-of-the-art method."
"Through a random selection of 600 cases, we assess the average, maximum, minimum, and standard deviation of entity distances with and without document nodes."
Quotes
"The proposed model outperforms existing methods on three public DocRE benchmarks."
"Our model can achieve leading performance in DocRE data in the general domain."