Domain-Specific NER for Fluorinated Materials: A Hybrid Approach with Adversarial Training and Dynamic Contextual Embeddings

In the research and production of fluorinated materials, large volumes of unstructured textual data are generated, characterized by high heterogeneity and fragmentation. These issues hinder systematic knowledge integration and efficient utilization. Constructing a knowledge graph for fluorinated materials processing is essential for enabling structured knowledge management and intelligent applications. Among its core components, Named Entity Recognition (NER) plays an essential role, as its accuracy directly impacts relation extraction and semantic modeling, which ultimately affects the knowledge graph construction for fluorinated materials. However, NER in this domain faces challenges such as fuzzy entity boundaries, inconsistent terminology, and a lack of high-quality annotated corpora. To address these problems, (i) We first construct a domain-specific NER dataset by combining manual annotation with an improved Easy Data Augmentation (EDA) strategy; (ii) Secondly, we propose a novel model, RRC-ADV, which integrates RoBERTa-wwm for dynamic contextual word representation, adversarial training to improve robustness against boundary ambiguity, and a Residual BiLSTM (ResBiLSTM) to enhance sequential feature modeling. Further, a Conditional Random Field (CRF) layer is incorporated for globally optimized label prediction. Experimental results demonstrate that RRC-ADV achieves an average F1 score of 89.23% on the self-constructed dataset, significantly outperforming baseline models. The model exhibits strong robustness and adaptability within the domain of fluorinated materials. Our work enhances the accuracy of NER in the fluorinated materials processing domain and paves the way for downstream tasks such as relation extraction in knowledge graph construction.