用大模型自动估算动词句式频率，速度快且覆盖广。
A Scalable Pipeline for Estimating Verb Frame Frequencies Using Large Language Models
- 用大模型生成含476个动词的语料，再让模型扮演语言学家分析句法结构。
- 在多个数据集上优于两种主流句法解析器，资源消耗远低于人工标注。
- 支持自定义扩展至新动词、句式或语言，适合语言学与自然语言处理研究者。
我们提出一种自动化流水线，用于估算动词句式频率（VFFs），即动词在特定句法结构中出现的频率。VFFs能揭示人类与机器语言系统中的句法特征，但现有工具在规模、准确率或可及性方面受限。我们利用大语言模型（LLMs）生成包含476个英语动词的句子语料，再通过指令让另一大模型像专家语言学家一样分析这些句子的句法结构。该流水线在多个评估数据集上优于两种广泛使用的句法解析器，且所需资源远少于人工标注（金标准），实现了快速、可扩展的VFF估算。基于此，我们构建了一个新的VFF数据库，覆盖更广的动词范围，具备更细粒度的句法区分，并明确给出心理语言学中常见句式交替的相对频率。该方法易于定制和扩展，适用于新动词、新句式乃至其他语言。本文作为自动化句式频率估算的范例，已开源全部代码与数据，以支持后续研究。
原文摘要 · Abstract (English)
We present an automated pipeline for estimating Verb Frame Frequencies (VFFs), the frequency with which a verb appears in particular syntactic frames. VFFs provide a powerful window into syntax in both human and machine language systems, but existing tools for calculating them are limited in scale, accuracy, or accessibility. We use large language models (LLMs) to generate a corpus of sentences containing 476 English verbs. Next, by instructing an LLM to behave like an expert linguist, we had it analyze the syntactic structure of the sentences in this corpus. This pipeline outperforms two widely used syntactic parsers across multiple evaluation datasets. Furthermore, it requires far fewer resources than manual parsing (the gold-standard), thereby enabling rapid, scalable VFF estimation. Using the LLM parser, we produce a new VFF database with broader verb coverage, finer-grained syntactic distinctions, and explicit estimates of the relative frequencies of structural alternates commonly studied in psycholinguistics. The pipeline is easily customizable and extensible to new verbs, syntactic frames, and even other languages. We present this work as a proof of concept for automated frame frequency estimation, and release all code and data to support future research.
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