---
title: "英语科技论文摘要语步结构语料库构建研究(Research on"
description: "Rhetorical moves are functional segments of academic texts, such as background, aim, method and result. 29 references."
authors: ["Hongzheng Li", "Ruojin Wang", "Chong Feng", "Fang Liu"]
year: 2024
venue: "Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)"
source: "https://aclanthology.org/2024.ccl-1.66/"
licence: "CC BY 4.0 (ACL Anthology)"
licence_url: "https://creativecommons.org/licenses/by/4.0/"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/en/papers/li-2024-research-construction-corpus-move-structures/"
---
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# 英语科技论文摘要语步结构语料库构建研究(Research on Construction of Corpus for Move Structures in Abstracts of English Scientific Research Articles)

**Authors:** Hongzheng Li, Ruojin Wang, Chong Feng and [Fang Liu](https://md.research.elamur.ai/free/en/authors/fang-liu/) · **Year:** 2024 · **Venue:** [Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)](https://md.research.elamur.ai/free/en/venues/ccl/) ([Chinese National Conference on Computational Linguistics 2024](https://md.research.elamur.ai/free/en/venues/ccl/2024/)) · **Publisher:** [Chinese Information Processing Society of China](https://md.research.elamur.ai/free/en/publishers/chinese-information-processing-society-of-china/) · **Licence:** [CC BY 4.0 (ACL Anthology)](https://creativecommons.org/licenses/by/4.0/)

**Links:** [Publisher's page](https://aclanthology.org/2024.ccl-1.66/) · [PDF](https://aclanthology.org/2024.ccl-1.66.pdf)

## Summary
Rhetorical moves are functional segments of academic texts, such as background, aim, method and result. They matter for genre analysis and for teaching academic writing, but annotated move data is scarce and usually covers one small domain. The authors built RAAMove, a public corpus of English research-article abstracts tagged with moves. It covers NLP and CV (ACL 2020–2022 long papers, AAAI 2022 CV track) and two engineering fields drawn from top journals.

They adapted Hyland's five-move scheme into eight labels after a pilot study showed gaps. Annotation ran in two phases:
- **Manual seed phase:** annotators produced a high-quality starting set by hand.
- **Main phase:** a BERT classifier with word-saliency attention pre-labelled sentences, humans corrected its output, and the corrections fed back into training.

The resource holds 4,670 abstracts and about 34k moves. The authors also compare move distributions across disciplines. In move-recognition experiments, the BERT model beat GPT-4 and Claude 3 on F1 for every move type. This supports the case for a dedicated annotated resource for scientific information extraction and writing feedback.

## Key points
- The eight move labels are BAC, GAP, PUR, MTD, RST, CLN, IMP and CTN. CTN (Contribution) was added for statements such as code releases, which fit none of Hyland's categories.
- The corpus holds 2,670 AI abstracts (1,340 NLP, 1,330 CV) and 2,000 engineering abstracts, for 33,988 annotated moves in total. It is released on GitHub (ljk1228/RAAMove) as JSONL with character-offset spans, exported from doccano.
- MTD is the most frequent move at about 34%, and IMP is the rarest at under 1%. PUR is the move present in the most abstracts, because an abstract usually states its aim once but describes its method over several sentences.
- Engineering abstracts have longer sentences (about 202 words per abstract versus 143 in AI) and more MTD sentences. They contain less background, gap and contribution material. AI abstracts mention open-sourced code and data more often.
- The authors attribute the LLMs' weaker, unstable results to two choices: they used only the chat web interfaces, and their prompts carried few cues, such as words like "demonstrate" that signal a CLN move.

## Interesting ideas
- A small pilot annotation run exposed recurring sentences that the inherited theory could not label, and the scheme was extended before scaling up. This avoids forcing data into ill-fitting classes.
- Per-word saliency scores toward each class were injected as an extra input embedding beside the token, segment and position embeddings. This makes cue words both influential and inspectable.
- Annotation was bootstrapped from a hand-labelled seed. A trained model then pre-labelled the remaining data, and human corrections were looped back to retrain it, growing the resource quickly without giving up quality control.
- Statistics were reported two ways: total occurrences, and the share of documents containing each class. The gap between the two exposed structural facts, such as aims being stated once while methods span many sentences.
- Move order in abstracts is regular (background, gap, purpose, method, result, conclusion) and differs by discipline. The text's discourse skeleton is therefore predictable and could act as a prior.

## For Mova

How these ideas could serve Mova, our small open language model — its files, stages and tests:

- **I1** (frames): Run a pilot pass of speech-act or frame labelling on real texts before freezing the label inventory, and add classes only where unlabelable cases recur.
- **I2** (frames): A per-token "cue strength" signal for speech-act or discourse-role classification could double as the explanation for why a sentence received its label.
- **I3** (learn): Silver labels from the witness parsers could go through the same correct-and-retrain cycle, with logged corrections serving as training signal for repair rules.
- **I4** (world): When profiling texts or event sequences, report both counts and per-document presence to tell repeated elaboration apart from ubiquity.
- **I5** (back): When generating summaries or abstracts from the meaning graph, order content by a move template suited to the genre or field.

*Our summary of the paper, in our own words — not its text. The work belongs to its authors; read it at the links above.*

📚 Shelves (2): [🎭 Arts and Humanities](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/) · 16,039 works · [🗣️ Language and Linguistics](https://md.research.elamur.ai/free/en/shelves/arts-and-humanities/language-and-linguistics/) · 15,891 works

🧭 Topics (1): [English](https://md.research.elamur.ai/free/en/topics/english/) · 2,263 works

## Cite

Cite as: Hongzheng Li, Ruojin Wang, Chong Feng and Fang Liu. 2024. 英语科技论文摘要语步结构语料库构建研究(Research on Construction of Corpus for Move Structures in Abstracts of English Scientific Research Articles). Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference).

The paper itself, not this page. BibTeX from the library's record of the work.

```bibtex
@inproceedings{li2024research,
  title = {{英语科技论文摘要语步结构语料库构建研究(Research on Construction of Corpus for Move Structures in Abstracts of English Scientific Research Articles)}},
  author = {Hongzheng Li, Ruojin Wang, Chong Feng and Fang Liu},
  booktitle = {Proceedings of the 23rd Chinese National Conference on Computational Linguistics (Volume 1: Main Conference)},
  year = {2024},
  url = {https://aclanthology.org/2024.ccl-1.66/}
}
```

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## References

29 references, 3 of them with a card of ours. ↗ leads to the work, «find» searches for one we have no id for yet.

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*Updated 1 November 2026*