“语料库检索工具在语言学研究领域具有举足轻重的地位,对于高效获取信息至关重要。然而,当前国内语料库检索工具在语料库检索语言上缺乏统一标准,尤其支持语料库查询语言(CQL)的中文语料库检索工具相对稀缺。在使用不同分词粒度的语料库工具进行中文语料库检索时,会遇到噪声或数据召回难问题。为应对这些挑战,我们研发了支持多粒度分词的CQL 解析器系统CAMELS:一款支持CQL 语句检索,且兼容多粒度分词,支持非词典词检索的语料库检索引擎。经过多种分词器的测试,该引擎展现出了优异的召回率,并在性能上超越了BlackLab的检索速度,为语言学工作者提供了更加易用、精准的检索工具。”

Text simplification aims to make the text easier to understand by applying rewriting transformations. There has been very little research on Chinese text simplification for a long time. The lack of generic evaluation data is an essential reason for this phenomenon. In this paper, we introduce MCTS, a multi-reference Chinese text simplification dataset. We describe the annotation process of the dataset and provide a detailed analysis. Furthermore, we evaluate the performance of several unsupervised methods and advanced large language models. We additionally provide Chinese text simplification parallel data that can be used for training, acquired by utilizing machine translation and English text simplification. We hope to build a basic understanding of Chinese text simplification through the foundational work and provide references for future research. All of the code and data are released at https://github.com/blcuicall/mcts/.