From Text to CQL: Bridging Natural Language and Corpus Search Engine
<|reference_start|>From Text to CQL: Bridging Natural Language and Corpus Search Engine: 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.<|reference_end|>
@article{lu2024from,
    title={From Text to CQL: Bridging Natural Language and Corpus Search Engine},
    author={Luming Lu, Jiyuan An, Yujie Wang, Liner yang, Cunliang Kong, Zhenghao
  Liu, Shuo Wang, Haozhe Lin, Mingwei Fang, Yaping Huang and Erhong Yang},
    journal={arXiv preprint arXiv:2402.13740},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.13740},
    primaryClass={cs.CL}
}