Integrating Graph-Based Representations with Deep Contextual Models for Text Classification
Year: 2025
Type: conference-paper
Abstract: Accurate text classification requires both deep contextual understanding and structural representation of language. This study explores a hybrid approach that integrates transformer-based embeddings with graph-based neural architectures to enhance text classification performance. By leveraging pre-trained language models for feature extraction and applying graph convolution techniques for relational modeling, the proposed method captures both semantic meaning and structural dependencies in text. Experimental results demonstrate improved classification accuracy over traditional approaches, highlighting the effectiveness of combining deep contextual learning with graph-based representations in NLP tasks
Author: Sumit Mamtani
Institution: New York University
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: gold
OpenAlex ID: W4412849958