KDPG-Enhanced MRC Framework for Scientific Entity Recognition in Survey Papers

Maodi Hu, Li Qian, Zhijun Chang, Zhixiong Zhang

Abstract: Scientific survey papers play a pivotal role in advancing knowledge and scientific progress by providing concise summaries and analyses of research trends and findings. To facilitate better knowledge organization and analysis, we have undertaken the challenge of defining the scientific entity recognition task for survey papers and carefully curated a dataset that closely emulates real-world scenarios. The scientific entity recognition task presents unique challenges, including multi-label, low-resource, and nested scenarios. To address these challenges, we propose a unified framework based on the machine reading comprehension (MRC) paradigm. This framework not only supports nested and multi-label settings but also enables the effective transfer of information from high-resource categories to low-resource ones, ensuring adaptability and robustness. To further enhance performance, we introduce the Knowledge-Driven Prototype Guidance (KDPG) module, seamlessly integrated into a two-phase learning strategy. The KDPG module leverages prior knowledge and acts as an initial prototype-based manifold constraint, effectively harnessing the power of few-shot learning capabilities. Through this integration, our approach complements the classification learning tasks for entity recognition, resulting in improved accuracy and efficiency. Our experimental results validate the effectiveness of the proposed KDPG-enhanced MRC framework, showcasing its leading performance on publicly available datasets and our collected scientific survey paper dataset.

External IDs:dblp:journals/taslp/HuQCZ24