# A Chinese Corpus for Fine-grained Entity Typing

arXiv: https://arxiv.org/abs/2004.08825 · HTML: https://mlsanity.com/paper/2004.08825/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 19 Apr 2020.

## Built on by (most cited first, 7 of 7)

- [Ultra-Fine Entity Typing with Weak Supervision from a Masked Language Model](https://mlsanity.com/paper/2106.04098/) (2021)
- [SeqGPT: An Out-of-the-box Large Language Model for Open Domain Sequence Understanding](https://mlsanity.com/paper/2308.10529/) (2023)
- [Generative Entity Typing with Curriculum Learning](https://mlsanity.com/paper/2210.02914/) (2022)
- [Cross-lingual Inference with A Chinese Entailment Graph](https://mlsanity.com/paper/2203.06264/) (2022)
- [Recall, Expand and Multi-Candidate Cross-Encode: Fast and Accurate Ultra-Fine Entity Typing](https://mlsanity.com/paper/2212.09125/) (2022)
- [From Ultra-Fine to Fine: Fine-tuning Ultra-Fine Entity Typing Models to Fine-grained](https://mlsanity.com/paper/2312.06188/) (2023)
- [Cross-Lingual Fine-Grained Entity Typing](https://mlsanity.com/paper/2110.07837/) (2021)

## Abstract (arXiv)

Fine-grained entity typing is a challenging task with wide applications. However, most existing datasets for this task are in English. In this paper, we introduce a corpus for Chinese fine-grained entity typing that contains 4,800 mentions manually labeled through crowdsourcing. Each mention is annotated with free-form entity types. To make our dataset useful in more possible scenarios, we also categorize all the fine-grained types into 10 general types. Finally, we conduct experiments with some neural models whose structures are typical in fine-grained entity typing and show how well they perform on our dataset. We also show the possibility of improving Chinese fine-grained entity typing through cross-lingual transfer learning.