[Purpose/Significance] Scientific articles are an important tool for recording the process of scientific problem formulation and solution. And the research problem sentences in scientific articles play an important role in revealing the specific content of scientific problems and understand the research themes of articles. Automatic recognition of research question sentences in scientific articles is an important task in scientific text mining. [Method/Process] In order to automatically recognize research question sentences, a semi-automatic annotation strategy based on iteration was firstly proposed. Combined with the confidence of model prediction, manual proofreading was carried out to annotate the data of research question sentences. On this basis, a sentence classification model was designed using a BERT-CNN architecture, which introduced BERT to generate text vectors and used CNN to extract text features. Then, the effectiveness of the model was verified by comparison with experiments and the benchmark model. [Result/Conclusion] A large-scale and available dataset was constructed based on the proposed annotation strategy, and the precision reached 95% after manual inspection. As for the recognition model based on the BERTCNN architecture, the F1 value reaches 94.8% in the research question sentence recognition task. This paper provides high-quality data support and effective modeling methods for the mining and analysis of research questions in scientific articles.
Li Xuesi
,
Zhang Zhixiong
,
Liu Yi
,
Wang Yufei
. A Study on the Method of Identifying Research Question Sentences in Scientific Articles[J]. Library and Information Service, 2023
, 67(9)
: 132
-140
.
DOI: 10.13266/j.issn.0252-3116.2023.09.014
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