Quintuple Extraction Method for Scientific Papers Based on Feature Words Adversarial Scheme

When extracting entities, relations, and their associated words from scientific literature, it is imperative to consider the supporting role of feature words on the extraction results. These feature words can provide local semantic information and be combined with the global feature representation of the sentence, improving the accuracy of information extraction. However, existing methods, when fusing local semantic feature words with global features, due to ineffective distinction between the influence of feature words and non-feature words, result in limited enhancement on model performance. To solve this problem, we propose a feature words adversarial scheme (FWAS) with dual pointer method. This method implements a dynamic filtering mechanism for feature words through feature pointers, in order to semantically enhance the encoding of the original text. Simultaneously, an inverse feature pointer is designed to establish a negative weight decay mechanism, weakening interference of non-key vocabulary. During joint training, annotation information for entity relations is introduced to supervise the dual feature selection mechanism. Experimental results on three public scientific information extraction datasets demonstrate that our method consistently outperforms strong baselines, achieving up to 4.9% improvement in F1-score. This method offers a new perspective for information extraction tasks in scientific and technical literature and provides scalable optimization directions for subsequent research.