RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts

• URL: http://arxiv.org/abs/2403.15872v1
• Date: Sat, 23 Mar 2024 15:43:30 GMT
• Title: RAAMove: A Corpus for Analyzing Moves in Research Article Abstracts
• Authors: Hongzheng Li, Ruojin Wang, Ge Shi, Xing Lv, Lei Lei, Chong Feng, Fang Liu, Jinkun Lin, Yangguang Mei, Lingnan Xu,
• Abstract summary: RAAMove is a comprehensive corpus dedicated to the annotation of move structures in Research Article (RA) abstracts. The corpus is constructed through two stages: first, expert annotators manually annotate high-quality data; then, based on the human-annotated data, a BERT-based model is employed for automatic annotation. The result is a large-scale and high-quality corpus comprising 33,988 annotated instances.
• Score: 9.457460355411582
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Move structures have been studied in English for Specific Purposes (ESP) and English for Academic Purposes (EAP) for decades. However, there are few move annotation corpora for Research Article (RA) abstracts. In this paper, we introduce RAAMove, a comprehensive multi-domain corpus dedicated to the annotation of move structures in RA abstracts. The primary objective of RAAMove is to facilitate move analysis and automatic move identification. This paper provides a thorough discussion of the corpus construction process, including the scheme, data collection, annotation guidelines, and annotation procedures. The corpus is constructed through two stages: initially, expert annotators manually annotate high-quality data; subsequently, based on the human-annotated data, a BERT-based model is employed for automatic annotation with the help of experts' modification. The result is a large-scale and high-quality corpus comprising 33,988 annotated instances. We also conduct preliminary move identification experiments using the BERT-based model to verify the effectiveness of the proposed corpus and model. The annotated corpus is available for academic research purposes and can serve as essential resources for move analysis, English language teaching and writing, as well as move/discourse-related tasks in Natural Language Processing (NLP).

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