---
title: "动词驱动事件的共指关系中文语料库构建及大模型评测 — 王皓泽 2026"
description: "Most event coreference work is closed-domain: it only links mentions of events that fit a fixed type inventory (e.g., ACE2005, ECB+, MAVEN-ERE), so many…"
authors: ["王皓泽", "辛欣"]
year: 2026
venue: "中文信息学报 (Journal of Chinese Information Processing)"
source: "http://jcip.cipsc.org.cn/article/id/zwxxxb_4010"
licence: "Free access (jcip.cipsc.org.cn), no open licence stated — readonly; © 中文信息学报"
summary_licence: "Elamur Cosmic License"
canonical: "https://research.elamur.ai/free/papers/wang-2026-chinese-coreference-corpus/"
---
[Elamur Research](https://md.research.elamur.ai/) · [this page in HTML](https://research.elamur.ai/free/papers/wang-2026-chinese-coreference-corpus/)

[home](https://md.research.elamur.ai/) / [papers](https://md.research.elamur.ai/free/papers/) / [2026](https://md.research.elamur.ai/free/papers/2026/page/5/) / 动词驱动事件的共指关系中文语料库构建及大模型评测

# 动词驱动事件的共指关系中文语料库构建及大模型评测

**Authors:** 王皓泽, 辛欣 · **Year:** 2026 · **Venue:** 中文信息学报 (Journal of Chinese Information Processing) · **Licence:** Free access (jcip.cipsc.org.cn), no open licence stated — readonly; © 中文信息学报

**Links:** [Publisher's page](http://jcip.cipsc.org.cn/article/id/zwxxxb_4010) · [PDF](http://jcip.cipsc.org.cn/cn/article/pdf/preview/zwxxxb_4010.pdf)

## Summary
Most event coreference work is closed-domain: it only links mentions of events that fit a fixed type inventory (e.g., ACE2005, ECB+, MAVEN-ERE), so many real-world actions and static states go unaddressed. The authors avoid type inventories altogether and define coreference through the event's linguistic carrier, starting with verb–verb links in Chinese. Their annotation guideline uses HowNet sememes for verb meaning and Yuan Yulin's argument-role system for semantic roles. It separates **strong coreference** (identical meaning and matching relevant roles) from **weak coreference**, where one event contains or modifies the other. Using this scheme, they manually annotated a Chinese corpus built from People's Daily texts. They then tested a two-stage pipeline. Mention detection relies on constituency parsing, and coreference prediction uses large language models adapted through prompting, instruction tuning or knowledge distillation. Distillation performed best. Fine-tuned small models came close to the LLMs while running much faster. The work provides an open-domain Chinese benchmark and shows how far current LLMs still are from solving the task.

## Key points
- Corpus: 1,000 documents (380k+ characters), 29,250 verbs, 1,858 strong coreference chains and 994 weak coreference verb pairs. It is released on GitHub under NP-NET-research (Reference-Resolution-for-Chinese-Verbs).
- Weak coreference has three subtypes: verb-sense inclusion, role inclusion and role change. Roles that affect coreference are agent/patient, time, location, range and proposition. Roles such as instrument, material, manner and cause are treated as irrelevant.
- Mention detection via constituency parsing reaches 96.9% F1.
- Average coreference-prediction F1 was 50.0% with prompt engineering, 64.4% with instruction tuning and 71.7% with knowledge distillation. The tested models were Zhipu Qingyan, ChatGLM3-6B and QWen-7B-Chat.
- Supervised BERT-style small models score slightly below instruction-tuned LLMs but are far more efficient at inference. The paper appears in vol. 39, no. 11 (Nov. 2025) of the journal.

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

Fields: [Linguistics & languages](https://md.research.elamur.ai/free/shelves/linguistics/) · [Natural language processing](https://md.research.elamur.ai/free/shelves/nlp/)

Topics: [Coreference resolution](https://md.research.elamur.ai/free/topics/coreference-resolution/)

## Cite

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

```bibtex
@article{2026,
  title = {{动词驱动事件的共指关系中文语料库构建及大模型评测}},
  author = {王皓泽 and 辛欣},
  journal = {中文信息学报 (Journal of Chinese Information Processing)},
  year = {2026},
  url = {http://jcip.cipsc.org.cn/article/id/zwxxxb_4010}
}
```

- [← previous 从感知到推理：深度思考赋能多模态大语言模型](https://md.research.elamur.ai/free/papers/zhu-2026-perception-reasoning-deep/)
- [next → 基于事实验证的五链式机器翻译幻觉检测智能体](https://md.research.elamur.ai/free/papers/wei-2026-fact-verification-five/)
- [↑ back to papers of 2026](https://md.research.elamur.ai/free/papers/2026/page/5/)