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| Identification of Problem and Method in Scientific Papers Based on Formulaic Expression Desensitization and Enhanced Boundary Recognition |
| Zhang Yingyi1, Zhang Chengzhi2 |
1.Department of Archives and E-government, School of Social Science, Soochow University, Suzhou 215123
2.Department of Information Management, School of Economics and Management, Nanjing University of Science and Technology, Nanjing 210094 |
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Abstract Problems and methods are crucial components of scientific papers and play a significant role in the organization, management, retrieval, and evaluation of scientific papers. To alleviate the formulaic expression dependency and word boundary recognition errors in problem and method recognition methods, we propose a model combined with formulaic expression desensitization and enhanced boundary recognition. Specifically, formulaic expression desensitization is achieved through data augmentation methods, whereas boundary enhancement utilizes pointer networks and sequence labeling models. With open access to scientific papers, researchers are utilizing full-text papers for entity recognition tasks. To demonstrate the importance of using full-text papers, this paper manually constructs an abstract and full-text annotated dataset in the field of natural language processing. Numerical and content-based metrics are designed to compare the problem, method, and their relationship extracted from two datasets. The results of ten-fold cross-validation experiments indicate that the proposed model outperforms baseline models such as SciBERT-BiLSTM-CRF significantly, with a macro-average F1 score improvement of 3.69 percentage points. When comparing entity recognition and relationship extraction results between abstracts and full texts, this paper shows that problem and method entities in abstracts have a broader semantic representation, whereas full texts contain more detailed entities and relationships that describe model design and training procedures.
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Received: 24 July 2023
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