Exploring Graph-Based Techniques in Text Data Processing: A Comprehensive Survey of NLP Advancements
Tuğba Çelikten, Aytuğ Onan · ICCK Transactions on Emerging Topics in Artificial Intelligence · 2026
Graph Neural Networks (GNNs) have become increasingly prominent in Natural Language Processing (NLP) due to their ability to model intricate relationships and contextual connections between texts. Unlike traditional NLP methods, which typically process text linearly, GNNs utilize graph structures to represent the complex relationships between texts more effectively. This capability has led to significant advancements in various NLP applications, such as social media interaction analysis, sentiment analysis, text classification, and information extraction. Notably, GNNs excel in scenarios with limited labeled data, often outperforming traditional approaches by providing deeper, context-aware solutions. Their versatility in handling different data types has made GNNs a popular choice in NLP research. In this study, we thoroughly explored the application of GNNs across various NLP tasks, demonstrating their advantages in understanding and representing text relationships. We also examined how GNNs address traditional NLP challenges, showcasing their potential to deliver more meaningful and accurate results. Our research underscores the value of GNNs as a potent tool in NLP and suggests future research directions to enhance their applicability and effectiveness further.