Natural Language Processing (NLP) technologies have revolutionized the way we
interact with information systems, with a significant focus on converting
natural language queries into formal query languages such as SQL. However, less
emphasis has been placed on the Corpus Query Language (CQL), a critical tool
for linguistic research and detailed analysis within text corpora. The manual
construction of CQL queries is a complex and time-intensive task that requires
a great deal of expertise, which presents a notable challenge for both
researchers and practitioners. This paper presents the first text-to-CQL task
that aims to automate the translation of natural language into CQL. We present
a comprehensive framework for this task, including a specifically curated
large-scale dataset and methodologies leveraging large language models (LLMs)
for effective text-to-CQL task. In addition, we established advanced evaluation
metrics to assess the syntactic and semantic accuracy of the generated queries.
We created innovative LLM-based conversion approaches and detailed experiments.
The results demonstrate the efficacy of our methods and provide insights into
the complexities of text-to-CQL task.

自然语言处理技术 (NLP) 在信息系统中的交互方式发生了革命性的变化，强调了将自然语言查询转化为 SQL 等形式查询语言，然而对于语料库查询语言 (CQL) 这一在文本语料中进行语言研究和详细分析的关键工具却未受到足够重视，手动构建 CQL 查询是一项复杂、耗时的任务，需要大量专业知识，这对研究人员和从业者都构成了重要挑战。本文提出了第一个文本到 CQL 任务，旨在自动将自然语言转化为 CQL。我们提出了一个全面的框架，包括一个特别策划的大规模数据集和利用大型语言模型 (LLMs) 进行有效的文本到 CQL 任务的方法。此外，我们建立了先进的评估指标来评估生成的查询的句法和语义准确性。我们提供了创新的 LLM 转换方法和详细的实验，并通过实验结果证明了我们方法的有效性，并提供了关于文本到 CQL 任务的复杂性的深入见解。