# FREDC: A Few-Shot Relation Extraction Dataset for Chinese
## 2 Related Work
In preparing the dataset for few-shot RE tasks, the training and testing subsets were reclassified based on relation types. Specifically, 30 relation types were allocated for training, while 14 were reserved for testing and validation. But this dataset is not applicable to most RE studies due to the fact that the data in this dataset are derived from a single domain and noisy data have been added. [PERSON] et al. [14] have delved into few-shot RE for Chinese-specific domains. They initiated this by creating Bridge-FewRel, a dataset derived from 1300 actual bridge inspection reports. The dataset consists of 8430 sentences that have been meticulously labeled by hand. It encompasses four entity categories and sixteen relationship categories, with each of the training and test sets including eight distinct relationship types. Following this, they developed TinyRel-CM, a dataset compiled from data sourced from Chinese healthcare websites., manually screened and labeled by five TCM students. The dataset was constructed with a total of four entities and 27 different binary relationships, where each relationship contains 50 instance samples. While these two datasets extend the study of few-shot RE on domain-specific scenarios, the datasets are smaller overall in terms of entity and relationship types, and the relationship definitions are simpler. [PERSON] et al. [15] have developed a Chinese social media dataset for few-shot RE. They gathered and annotated the data from various sources like web crawling and open-source projects, resulting in a dataset with 6000 entries and 10 + 1 (NOTA) categories. Each entry consists of texts with five distinct attributes. Furthermore, [PERSON] et al. [16] presented the TinyACD, a dataset for few-shot RE in ancient Chinese texts. The dataset was compiled from \"Shih Chi\", featuring 1600 data entries that were manually annotated by historians. This dataset has 11 entity pair types with 32 relation classes, of which twenty-four relationships are used in the training set and eight relationships are used in the test set. The dataset comprises sentences that are structurally intricate and frequently contain abbreviations. To ensure the highest level of quality, a third round of evaluation has been incorporated. The goal of TinyACD-RC is to mimic digital humanities research scenarios, offering a foundation for studying relations in ancient documents. Due to the specificity of the content of this dataset, the model can only learn the features of ancient Chinese through this dataset, which leads to a narrower use of this dataset. In the previously mentioned Chinese few-shot relation classification datasets, there are several notable problems. Their applicability is limited due to being constructed with specific research objectives in mind; the total volume of data and the variety of relations covered are both insufficient; and they often fail to meet the requirements of multiple few-shot tasks at the same time. To address these issues, our research reorganizes and enhances various existing information extraction datasets to create a dataset of superior quality. This dataset encompasses a rich diversity of relation types and a substantial amount of data. Our experimental results indicate that this dataset is suitable for a wide range of few-shot learning tasks and presents a new baseline of comparison for Chinese few-shot RE tasks.