[Purpose/Significance] Automatic recognition of scientific elements in project application abstracts is of great research significance for revealing scientific knowledge in science and technology projects. The recognition of these scientific elements relies on structured project abstract texts. However, the current lack of structured corpus resources for project abstract seriously restricts the further development of related research. Therefore, this paper intends to construct a move corpus of the project application abstract to provide data support for related research.[Method/Process] First, the project abstracts were summarized into four types of moves:background and problem, objective and task, methodological content, value and significance, then this paper summarized the iconic features that appear in the structure of each move and formulate a move annotation specification. Second, this study successively used rule-based and deep learning-based methods to assist in manual move structure annotation of project abstracts, and evaluate the quality of each round of annotated corpus.[Result/Conclusion] The two methods have annotated nearly 25,000 sentences, and the consistency coefficient of the corpus annotation reached 0.9839, which indicating that the corpus can basically distinguish different move structures among project abstracts and initially meet the basic requirements for corpus construction.
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