构建首个全类型多词表达语料库，助力精准识别与分析。
CoAM: Corpus of All-Type Multiword Expressions
- 通过人机协同流程构建高质量语料，覆盖多种多词表达类型。
- 1.3千句语料中动词类表达比名词类更易识别，模型表现优于旧方法。
- 支持细粒度类型标注，适合语言理解与评估研究者使用。
多词表达（MWE）指具有习语性质的多词序列，其识别对机器翻译等下游任务至关重要。现有数据集存在标注不一致、类型单一或规模有限等问题。为此，我们构建了CoAM：全类型多词表达语料库，包含1300条经过多阶段质量控制（人工标注、审核与自动化一致性检查）的句子。首次在MWE识别数据集中引入表达类型标签（如名词、动词），支持细粒度错误分析。标注通过自研接口生成器实现，灵活适配各类表达形式。实验表明，微调的大语言模型在CoAM上表现超越DiMSUM数据集上的SOTA方法MWEasWSD。进一步分析发现，动词类MWE比名词类更易识别，跨方法表现更优。
原文摘要 · Abstract (English)
Multiword expressions (MWEs) refer to idiomatic sequences of multiple words. MWE identification, i.e., detecting MWEs in text, can play a key role in downstream tasks such as machine translation, but existing datasets for the task are inconsistently annotated, limited to a single type of MWE, or limited in size. To enable reliable and comprehensive evaluation, we created CoAM: Corpus of All-Type Multiword Expressions, a dataset of 1.3K sentences constructed through a multi-step process to enhance data quality consisting of human annotation, human review, and automated consistency checking. Additionally, for the first time in a dataset of MWE identification, CoAM's MWEs are tagged with MWE types, such as Noun and Verb, enabling fine-grained error analysis. Annotations for CoAM were collected using a new interface created with our interface generator, which allows easy and flexible annotation of MWEs in any form. Through experiments using CoAM, we find that a fine-tuned large language model outperforms MWEasWSD, which achieved the state-of-the-art performance on the DiMSUM dataset. Furthermore, analysis using our MWE type tagged data reveals that Verb MWEs are easier than Noun MWEs to identify across approaches.
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