[目的/意义] 学术论文贡献句是体现论文创新性和学术价值的重要形式。以学术论文全文本和MeSH主题词为数据基础，利用自然语言处理和深度学习技术，实现学术论文贡献句识别，为学术文本创新贡献内容的细粒度挖掘奠定基础，对实现基于认知计算的学术论文评价具有重要的理论和现实意义。[方法/过程] 首先，以PubMed论文全文本为数据来源，抽取论文Mesh主题词，对论文贡献句进行要素分析和特征提取。其次，采用半自动方式实现标注数据。最后，基于Albert深度学习模型实现贡献句的自动识别。[结果/结论] 通过数据一致性检验证明实验标注的训练数据的可信性，实验结果表明，相较于其他深度学习模型，训练的自动识别模型能够更有效识别学术论文中贡献句。
[Purpose/significance] Contribution sentences of academic papers are elements to reflect the novelty and academic value of papers. This study takes the full text of academic papers and MeSH terms as data sources and uses natural language processing and deep learning techniques to achieve academic paper contribution sentence recognition. This study lays the foundation for fine-grained mining of innovative contents of academic texts, which is important for realizing the evaluation of academic papers based on cognitive computing.[Method/process] Firstly, the full-text PubMed papers were used as the data source for element analysis and feature extraction of the contributed sentences. Secondly, a semi-automatic approach was used to fulfill the data annotation. Finally, the automatic recognition of contributed sentences was realized based on Albert deep learning model.[Result/conclusion] The plausibility of the experimentally labeled training data is proved by the data consistency test, and the experimental results show that the automatic recognition model trained in this paper can identify the contribution sentences in academic papers more effectively compared with other deep learning models.
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