用大模型自动标注语言发展研究中的方式与结果动词，提升分析效率。
A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research
- 用语言学提示引导大模型生成句子级动词分类标注
- 在3个标准数据集上准确率达89.6%，覆盖436个动词类
- 适合做儿童语言习得、动词语义等研究的规模化分析
方式动词和结果动词反映了事件结构的不同方面，在儿童语言发展研究中可能具有重要价值。然而，由于缺乏大规模标注资源，这一区分难以实现规模化测量。本文提出一种计算方法，利用语言学启发式提示，通过大语言模型对MASC和InterCorp数据中的句子进行方式与结果动词标注，将覆盖范围从原有的VerbNet扩展至436个动词类。随后基于这些标注训练了一个RoBERTa分类器，并在三个保留的黄金标准数据集上进行评估，包括先前已标注项和新的专家标注集。模型在各项测试中表现良好，平均准确率达89.6%。本研究提供了一种可扩展的测量工具，可用于支持未来在语言发展及其他语料中的动词语义研究，但对边界案例、混合类型动词及下游应用仍需进一步验证。
原文摘要 · Abstract (English)
Manner and result verbs encode different aspects of event structure and have been discussed in developmental work as a potentially informative distinction for studying early verb learning. However, this distinction remains difficult to measure at scale because large annotated resources for manner and result classification are not currently available. We present a computational approach for identifying manner and result verbs in sentence context. Using linguistically informed prompts, we generate sentence-level annotations with large language models over data drawn from MASC and InterCorp, extending coverage from previously annotated portions of VerbNet to 436 classes. We then train a RoBERTa-based classifier on these annotations and evaluate it on three held-out gold-standard datasets, including previously annotated items and a new expert-annotated set. Across these evaluations, the model shows promising performance, with average accuracy up to 89.6%. We present this work as a scalable measurement tool that can support future research on verb semantics in developmental and other language datasets, while noting that further validation is needed for borderline cases, mixed manner/result verbs, and downstream developmental applications.
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