  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.
