A Scalable Tool for Measuring Manner and Result Verbs in Developmental Language Research

一种用于发展语言学研究中测量方式动词与结果动词的可扩展工具

Abstract: 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.

摘要： 方式动词（Manner verbs）和结果动词（Result verbs）编码了事件结构的不同方面，在发展语言学研究中，它们被视为研究早期动词习得的一个潜在重要区分点。然而，由于目前缺乏用于方式和结果分类的大规模标注资源，这一区分在规模化测量上仍然存在困难。

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.

我们提出了一种在句子语境中识别方式动词和结果动词的计算方法。通过使用基于语言学知识的提示词（prompts），我们利用大语言模型对来自 MASC 和 InterCorp 的数据进行了句子级标注，将覆盖范围从 VerbNet 此前已标注的部分扩展到了 436 个类别。

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%.

随后，我们基于这些标注训练了一个 RoBERTa 分类器，并在三个留出的黄金标准数据集上进行了评估，其中包括此前已标注的项目以及一个新的专家标注集。在这些评估中，该模型表现出了良好的性能，平均准确率高达 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.

我们将这项工作作为一种可扩展的测量工具，旨在支持未来在发展语言学及其他语言数据集中的动词语义研究。同时我们也指出，对于边缘案例、混合方式/结果动词以及下游的发展应用，仍需进行进一步的验证。