Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [Purpose/significance] Novelty measurement is an important part of scientific achievement evaluation. This paper aims to propose a method of novelty measurement and innovation type identification of scientific papers based on the combination of question and method. [Method/process] Based on the word frequency principle, this paper calculated the question novelty, method novelty and question-method combination novelty respectively, and then calculated the overall novelty of the paper by weight assignment. In addition, based on the theory of combination innovation, this study proposed four types of innovation from the perspective of scientific paper question-method combination and a method to identify the type of innovation according to the novelty value. [Result/conclusion] Finally, this paper conducts an empirical study based on more than 200,000 ACM papers from 1951 to 2018, and proves that the novelty measurement method and innovation category identification method proposed in this paper are scientific, reasonable and feasible.
Subjects: Library Science，Information Science >> Information Science submitted time 2023-04-01 Cooperative journals: 《图书情报工作》
Abstract： [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.
Hits
Hits
Downloads
Comment