Hybrid NLP And Deep Learning Techniques for Automated Knowledge Extraction from Scientific Literature

Abstract

The rapid expansion of scientific literature has created a critical need for intelligent systems capable of extracting structured knowledge from unstructured textual data. This study proposes a hybrid framework integrating Natural Language Processing (NLP) and deep learning techniques for automated knowledge extraction from scientific literature. The proposed approach combines contextual semantic representations with engineered linguistic and structural features using a Self-Attention-based Hybrid Feature Fusion Network. The framework processes scientific abstracts at the sentence level, enabling classification into rhetorical sections and facilitating structured knowledge reconstruction. By leveraging both deep contextual learning and statistical feature analysis, the model captures semantic dependencies and document structure effectively. Experimental evaluation demonstrates that the proposed hybrid model outperforms traditional baseline methods in terms of accuracy and F1-score, highlighting its robustness and generalization capability. Furthermore, the system enables the extraction of meaningful information segments and key domain-specific terms, supporting applications such as information retrieval, document summarization, and knowledge discovery. The results validate the effectiveness of integrating NLP and deep learning within a unified framework for scalable scientific text analysis. Overall, this work contributes to the advancement of automated knowledge extraction systems for large-scale scientific literature.