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FCDS: Fusing Constituency and Dependency Syntax for Document-Level Relation Extraction


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."

Key Insights Distilled From

by Xudong Zhu,Z... at arxiv.org 03-05-2024

https://arxiv.org/pdf/2403.01886.pdf
FCDS

Deeper Inquiries

How can incorporating both dependency and constituency syntax improve other NLP tasks beyond relation extraction

Incorporating both dependency and constituency syntax can improve other NLP tasks by providing a more comprehensive understanding of the text. Dependency syntax helps capture the relationships between words within a sentence, highlighting dependencies and structural information. On the other hand, constituency syntax organizes words hierarchically in a tree structure, enabling exploration of subsentences and capturing hierarchical relationships. For tasks like sentiment analysis, incorporating both types of syntax can help in understanding how different parts of a sentence contribute to overall sentiment. In machine translation, combining dependency and constituency syntax can assist in preserving syntactic structures during translation. For named entity recognition, leveraging both types of syntax can enhance entity identification by considering not only word dependencies but also their hierarchical organization within sentences. By fusing dependency and constituency syntax into various NLP tasks, models can benefit from a richer representation of text that captures both local dependencies and global structural information.

What are potential limitations or drawbacks of relying heavily on syntactic information for document-level relation extraction

While relying heavily on syntactic information for document-level relation extraction has its benefits, there are potential limitations to consider: Dependency Parsing Accuracy: The accuracy of the dependency parser plays a crucial role in extracting meaningful relations. If the parser makes errors or misinterprets certain dependencies, it could lead to incorrect relation predictions. Complexity: Document-level relation extraction involves analyzing multiple sentences with intricate inter-sentence connections. Depending too much on syntactic information alone may overlook semantic nuances that are essential for accurate relation extraction. Overfitting: Over-reliance on syntactic features may result in overfitting to specific patterns present in training data but not generalizable to unseen data or diverse domains. Computational Cost: Incorporating detailed syntactic parsing into models increases computational complexity and resource requirements, which may hinder scalability for large datasets or real-time applications.

How might advancements in syntactic parsing technology impact future developments in natural language processing research

Advancements in syntactic parsing technology have the potential to significantly impact future developments in natural language processing research: Improved Model Performance: Enhanced accuracy in parsing technologies will lead to better performance across various NLP tasks that rely on syntactic information such as parsing trees or graph-based representations. Enhanced Understanding: More sophisticated parsers can provide deeper insights into linguistic structures within text data, leading to more nuanced analyses and interpretations by NLP models. Efficient Information Extraction: Advanced parsing tools enable more efficient extraction of relevant information from text documents by accurately identifying relationships between entities at different levels (sentence level vs document level). 4Interdisciplinary Applications: Progression in syntactic parsing technology could facilitate interdisciplinary collaborations where NLP techniques are applied across fields like linguistics, cognitive science, psychology etc., broadening the scope of research possibilities. 5Robustness & Generalization: Improved parsers might make models less reliant solely on annotated training data while enhancing their ability to generalize well beyond seen examples due to better handling complex linguistic phenomena captured through advanced parses.
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