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 criti...

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Bibliographic Details
Published in:arXiv.org 2024-02
Main Authors: Lu, Luming, An, Jiyuan, Wang, Yujie, Liner yang, Kong, Cunliang, Liu, Zhenghao, Wang, Shuo, Lin, Haozhe, Fang, Mingwei, Huang, Yaping, Yang, Erhong
Format: Article
Language:English
Subjects:
Information systems
Language
Large language models
Natural language
Natural language processing
Queries
Query languages
Search engines
Online Access:Get full text
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Summary: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.
ISSN:2331-8422