From Text to CQL: Bridging Natural Language and Corpus Search Engine

• URL: http://arxiv.org/abs/2402.13740v1
• Date: Wed, 21 Feb 2024 12:11:28 GMT
• Title: From Text to CQL: Bridging Natural Language and Corpus Search Engine
• Authors: Luming Lu, Jiyuan An, Yujie Wang, Liner yang, Cunliang Kong, Zhenghao Liu, Shuo Wang, Haozhe Lin, Mingwei Fang, Yaping Huang and Erhong Yang
• Abstract summary: Corpus Query Language (CQL) is a critical tool for linguistic research and detailed analysis within text corpora. This paper presents the first text-to-CQL task that aims to automate the translation of natural language into CQL.
• Score: 27.56738323943742
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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