Automated recognition of innovative sentences in academic articles: semi-automatic annotation for cost reduction and SAO reconstruction for enhanced data

Research on innovative content within academic articles plays a vital role in exploring the frontiers of scientific and technological innovation while facilitating the integration of scientific and technological evaluation into academic discourse. To efficiently gather the latest innovative concepts, it is essential to accurately recognize innovative sentences within academic articles. Although several supervised methods for classifying article sentences exist, such as citation function sentences, future work sentences, and formal citation sentences, most of these methods rely on manual annotations or rule-based matching to construct datasets, often neglecting an in-depth exploration of model performance enhancement. To address the limitations of existing research in this domain, this study introduces a semi-automatic annotation method for innovative sentences (IS) with the assistance of expert comments information and proposes a data augmentation method by SAO reconstruction to augment the training dataset. Within this paper, we compared and analyzed the effectiveness of multiple algorithms for recognizing IS within academic articles. This study utilized the full text of academic articles as the research subject and employed the semi-automatic method to annotate IS for creating the training dataset. Then, this study validated the effectiveness of the semi-automatic annotation method through manual inspection and compared it with rule-based annotation methods. Additionally, the impacts of different augmentation ratios on model performance were also explored. The empirical results reveal the following: (1) The semi-automatic annotation method proposed in this study achieves an accuracy rate of 0.87239, ensuring the validity of annotated data while reducing the manual annotation cost. (2) The SAO reconstruction for data augmentation method significantly improved the accuracy of machine learning and deep learning algorithms in the recognition of IS. (3) When the augmentation ratio in the training set was set to 50%, the trained GPT-2 model was superior to other algorithms, achieving an ACC of 0.97883 in the test set and an F1 score of 0.95505 in practical application.