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RAAMove: Analyzing Moves in Research Article Abstracts


Core Concepts
RAAMove introduces a corpus for move analysis in research article abstracts to aid move identification and discourse analysis.
Abstract
Introduction: Move structures are crucial for effective communication in research articles. RAAMove Corpus: A multi-domain corpus dedicated to move structure annotation in research article abstracts. Construction Process: Manual annotation followed by BERT-based automatic annotation. Contributions: Development of a move structure annotation corpus, revision of move categories, and innovative BERT-based model. Experiments and Analysis: Move structure recognition experiments show the effectiveness of the proposed model. Comparison with ChatGPT: Our model outperforms ChatGPT in move structure identification tasks.
Stats
The corpus comprises 33,988 annotated instances.
Quotes
"One way that writers claimed significance was by opening their abstracts with a promotional statement." - Hyland (2007)

Key Insights Distilled From

by Hongzheng Li... at arxiv.org 03-26-2024

https://arxiv.org/pdf/2403.15872.pdf
RAAMove

Deeper Inquiries

How can move analysis benefit language learners beyond academic writing?

Move analysis can benefit language learners beyond academic writing by enhancing their overall communication skills. By understanding the structure and function of moves in written or spoken discourse, learners can improve their ability to organize ideas coherently, convey information effectively, and engage with different types of texts. This skill is transferable to various contexts such as professional communication, public speaking, and everyday interactions. Additionally, move analysis helps learners develop critical thinking skills as they learn to identify the purpose behind each move and how it contributes to the overall message of a text.

What are the limitations of focusing on moves within specific domains or journals?

Focusing on moves within specific domains or journals may lead to limited generalizability of findings. Different disciplines or journals may have unique conventions for organizing information in research articles, which could restrict the applicability of move analysis across diverse fields. Moreover, an exclusive focus on specific domains might overlook valuable insights that could be gained from comparing move structures across disciplines. This approach could also hinder interdisciplinary research efforts aimed at understanding broader patterns in academic discourse.

How can automated annotation tools like BERT impact future research on discourse analysis?

Automated annotation tools like BERT have the potential to revolutionize future research on discourse analysis by streamlining data processing and improving efficiency. These tools can handle large volumes of text data quickly and accurately, enabling researchers to analyze move structures in extensive corpora more effectively than manual methods alone. BERT's natural language processing capabilities allow for sophisticated pattern recognition and semantic understanding, leading to enhanced identification of rhetorical moves in texts. By leveraging these technologies, researchers can conduct more comprehensive studies on discourse features across different genres and languages with greater precision and speed.
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